diff --git a/conda-env/ci.yml b/conda-env/ci.yml index e999a1240..653f40f03 100644 --- a/conda-env/ci.yml +++ b/conda-env/ci.yml @@ -19,7 +19,7 @@ dependencies: - cdp=1.7.0 - eofs=2.0.0 - seaborn=0.12.2 - - enso_metrics=1.1.2 + - enso_metrics=1.1.4 - xcdat=0.7.3 - xmltodict=0.13.0 - setuptools=67.7.2 @@ -30,6 +30,7 @@ dependencies: - numdifftools - nc-time-axis - colorcet + - cmocean # ================== # Testing # ================== diff --git a/conda-env/dev.yml b/conda-env/dev.yml index 835752dfe..15a68b9ea 100644 --- a/conda-env/dev.yml +++ b/conda-env/dev.yml @@ -19,7 +19,7 @@ dependencies: - cdp=1.7.0 - eofs=2.0.0 - seaborn=0.12.2 - - enso_metrics=1.1.2 + - enso_metrics=1.1.4 - xcdat=0.7.3 - xmltodict=0.13.0 - setuptools=67.7.2 @@ -30,6 +30,7 @@ dependencies: - numdifftools - nc-time-axis - colorcet + - cmocean # ================== # Testing # ================== diff --git a/doc/jupyter/Demo/Demo_6_ENSO.ipynb b/doc/jupyter/Demo/Demo_6_ENSO.ipynb index f05200821..b34ba34da 100644 --- a/doc/jupyter/Demo/Demo_6_ENSO.ipynb +++ b/doc/jupyter/Demo/Demo_6_ENSO.ipynb @@ -89,63 +89,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Downloading: 'obs4MIPs_PCMDI_monthly/ECMWF/ERA-20C/mon/psl/gn/v20210727/psl_mon_ERA-20C_PCMDI_gn_190001-201012.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/ECMWF/ERA-20C/mon/psl/gn/v20210727/psl_mon_ERA-20C_PCMDI_gn_190001-201012.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/ECMWF/ERA-20C/mon/ts/gn/v20210727/ts_mon_ERA-20C_PCMDI_gn_190001-201012.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/ECMWF/ERA-20C/mon/ts/gn/v20210727/ts_mon_ERA-20C_PCMDI_gn_190001-201012.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/NOAA-ESRL-PSD/20CR/mon/psl/gn/v20210727/psl_mon_20CR_PCMDI_gn_187101-201212.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/NOAA-ESRL-PSD/20CR/mon/psl/gn/v20210727/psl_mon_20CR_PCMDI_gn_187101-201212.nc\n", 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from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/ESSO/TropFlux-1-0/mon/hfls/gn/v20210727/hfls_mon_TropFlux-1-0_PCMDI_gn_197901-201707.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/ESSO/TropFlux-1-0/mon/hfns/gn/v20210727/hfns_mon_TropFlux-1-0_PCMDI_gn_197901-201707.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/ESSO/TropFlux-1-0/mon/hfns/gn/v20210727/hfns_mon_TropFlux-1-0_PCMDI_gn_197901-201707.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/ESSO/TropFlux-1-0/mon/hfss/gn/v20210727/hfss_mon_TropFlux-1-0_PCMDI_gn_197901-201707.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/ESSO/TropFlux-1-0/mon/hfss/gn/v20210727/hfss_mon_TropFlux-1-0_PCMDI_gn_197901-201707.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/ESSO/TropFlux-1-0/mon/tas/gn/v20210727/tas_mon_TropFlux-1-0_PCMDI_gn_197901-201707.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: 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] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloading: 'obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/va/gn/v20210727/va_mon_ERA-INT_PCMDI_gn_198901-201001.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/va/gn/v20210727/va_mon_ERA-INT_PCMDI_gn_198901-201001.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/vas/gn/v20210727/vas_mon_ERA-INT_PCMDI_gn_197901-201903.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/vas/gn/v20210727/vas_mon_ERA-INT_PCMDI_gn_197901-201903.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/zg/gn/v20210727/zg_mon_ERA-INT_PCMDI_gn_198901-201001.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/ECMWF/ERA-INT/mon/zg/gn/v20210727/zg_mon_ERA-INT_PCMDI_gn_198901-201001.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/NOAA-NCEI/GPCP-2-3/mon/pr/gn/v20210727/pr_mon_GPCP-2-3_PCMDI_gn_197901-201907.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/NOAA-NCEI/GPCP-2-3/mon/pr/gn/v20210727/pr_mon_GPCP-2-3_PCMDI_gn_197901-201907.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/NASA-GSFC/TRMM-3B43v-7/mon/pr/gn/v20210727/pr_mon_TRMM-3B43v-7_PCMDI_gn_199801-201712.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/NASA-GSFC/TRMM-3B43v-7/mon/pr/gn/v20210727/pr_mon_TRMM-3B43v-7_PCMDI_gn_199801-201712.nc\n", - "Downloading: 'obs4MIPs_PCMDI_monthly/CNES/AVISO-1-0/mon/zos/gn/v20210727/zos_mon_AVISO-1-0_PCMDI_gn_199301-201912.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/obs4MIPs_PCMDI_monthly/CNES/AVISO-1-0/mon/zos/gn/v20210727/zos_mon_AVISO-1-0_PCMDI_gn_199301-201912.nc\n", - "Downloading: 'CMIP5_demo_data/psl_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/psl_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/ts_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/hfls_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/hfls_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/hfss_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/hfss_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/pr_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/pr_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/rlds_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/rlds_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/rlus_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/rlus_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/rlut_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/rlut_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/rsds_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/rsds_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/rsdt_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/rsdt_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/rsus_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/rsus_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/rsut_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/rsut_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/sftlf_fx_ACCESS1-0_amip_r0i0p0.nc\n", - "Downloading: 'CMIP5_demo_data/tauu_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/tauu_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", - "Downloading: 'CMIP5_demo_data/zos_Omon_ACCESS1-0_historical_r1i1p1_185001-200512.nc' from 'https://pcmdiweb.llnl.gov/pss/pmpdata/' in: demo_data/CMIP5_demo_data/zos_Omon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\n", "All files downloaded\n" ] } @@ -189,6 +132,23 @@ "generate_parameter_files(demo_data_directory, demo_output_directory, filenames=filenames)" ] }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d56dfa78", + "metadata": {}, + "outputs": [], + "source": [ + "# To open and display one of the graphics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg\n", + "from matplotlib import rcParams\n", + "\n", + "import os\n", + "\n", + "%matplotlib inline" + ] + }, { "cell_type": "markdown", "id": "6dbcc822", @@ -218,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "1c1abe78", "metadata": {}, "outputs": [ @@ -279,7 +239,7 @@ " The name of the folder where all runs will be stored.\n", " (default: None)\n", " --case_id CASE_ID version as date, e.g., v20191116 (yyyy-mm-dd)\n", - " (default: v20240702)\n", + " (default: v20241204)\n", " --obs_catalogue OBS_CATALOGUE\n", " obs_catalogue JSON file for CMORized observation,\n", " default is None (default: None)\n", @@ -320,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "49a4ac2c", "metadata": {}, "outputs": [ @@ -357,7 +317,7 @@ "\n", "json_name = '%(mip)_%(exp)_%(metricsCollection)_%(case_id)_%(model)_%(realization)'\n", "netcdf_name = json_name\n", - "nc_out = False\n", + "nc_out = True\n", "\n" ] } @@ -377,11 +337,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "334a0272", - "metadata": { - "scrolled": true - }, + "execution_count": 8, + "id": "5940ef4e", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -397,14 +355,14 @@ "debug: False\n", "obs_cmor: True\n", "obs_cmor_path: demo_data/obs4MIPs_PCMDI_monthly\n", - "egg_pth: /home/ordonez4/miniconda3/envs/pmp_test/share/pmp\n", + "egg_pth: /Users/lee1043/mambaforge/envs/pmp_devel_20241202/share/pmp\n", "output directory for graphics:demo_output/basicTestEnso/ENSO_perf\n", "output directory for diagnostic_results:demo_output/basicTestEnso/ENSO_perf\n", "output directory for metrics_results:demo_output/basicTestEnso/ENSO_perf\n", "list_variables: ['pr', 'sst', 'taux']\n", "list_obs: ['AVISO-1-0', 'ERA-INT', 'GPCP-2-3', 'HadISST-1-1']\n", "PMPdriver: dict_obs readin end\n", - "Process start: Tue Jul 2 14:34:34 2024\n", + "Process start:Wed Dec 4 00:54:46 2024\n", "models: ['ACCESS1-0']\n", " ----- model: ACCESS1-0 ---------------------\n", "PMPdriver: var loop start for model ACCESS1-0\n", @@ -527,74 +485,118 @@ "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, BiasPrLatRmse = ACCESS1-0_r1i1p1 and GPCPv2.3\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeCollection: metric = BiasPrLonRmse\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, BiasPrLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, BiasPrLonRmse = ACCESS1-0_r1i1p1 and GPCPv2.3\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeCollection: metric = BiasSstLonRmse\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, BiasSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, BiasSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[94m ComputeCollection: metric = BiasTauxLonRmse\u001b[0m\n" + "\u001b[93m\n", + "\u001b[93m\n", + " %%%%% ----- %%%%%\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", + " %%%%% ----- %%%%%\n", + "\u001b[0m\n", + "\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[94m ComputeCollection: metric = BiasTauxLonRmse\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarRMSmetric, BiasTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[94m ComputeCollection: metric = EnsoAmpl\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m\n", + "\u001b[93m\n", + " %%%%% ----- %%%%%\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", + " %%%%% ----- %%%%%\n", + "\u001b[0m\n", + "\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[94m ComputeCollection: metric = EnsoDuration\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/ordonez4/miniconda3/envs/pmp_test/lib/python3.10/site-packages/cdms2/MV2.py:318: Warning: arguments order for compress function has changed\n", + "/Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/cdms2/MV2.py:318: Warning: arguments order for compress function has changed\n", "it is now: MV2.copmress(array,condition), if your code seems to not react or act wrong to a call to compress, please check this\n", - " warnings.warn(\n", - "INFO::2024-07-02 14:42::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n", - "2024-07-02 14:42:16,724 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n", - "2024-07-02 14:42:16,724 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n", - "INFO::2024-07-02 14:42::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", - "2024-07-02 14:42:29,862 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", - "2024-07-02 14:42:29,862 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n" + " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[94m ComputeMetric: oneVarRMSmetric, BiasTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[94m ComputeCollection: metric = EnsoSeasonality\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[94m ComputeCollection: metric = EnsoAmpl\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeCollection: metric = EnsoDuration\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeCollection: metric = EnsoSeasonality\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n", + "\u001b[93m\n", + "\u001b[93m\n", + " %%%%% ----- %%%%%\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", + " %%%%% ----- %%%%%\n", + "\u001b[0m\n", + "\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeCollection: metric = EnsoSstDiversity_2\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", @@ -611,51 +613,83 @@ "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "operands could not be broadcast together with shapes (10,120) (0,) \n", "\u001b[94m ComputeCollection: metric = EnsoSstSkew\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m\n", + "\u001b[93m\n", + " %%%%% ----- %%%%%\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", + " %%%%% ----- %%%%%\n", + "\u001b[0m\n", + "\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeCollection: metric = EnsoSstTsRmse\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstTsRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstTsRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "operands could not be broadcast together with shapes (10,120) (0,) \n", "\u001b[94m ComputeCollection: metric = SeasonalPrLatRmse\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, SeasonalPrLatRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, SeasonalPrLatRmse = ACCESS1-0_r1i1p1 and GPCPv2.3\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageZonal\u001b[0m\n", "\u001b[94m ComputeCollection: metric = SeasonalPrLonRmse\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, SeasonalPrLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, SeasonalPrLonRmse = ACCESS1-0_r1i1p1 and GPCPv2.3\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeCollection: metric = SeasonalSstLonRmse\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, SeasonalSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, SeasonalSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m\n", + "\u001b[93m\n", + " %%%%% ----- %%%%%\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", + " %%%%% ----- %%%%%\n", + "\u001b[0m\n", + "\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeCollection: metric = SeasonalTauxLonRmse\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, SeasonalTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", @@ -664,8 +698,165 @@ "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO::2024-12-04 01:02::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "2024-12-04 01:02:55,116 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "2024-12-04 01:02:55,116 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "INFO::2024-12-04 01:03::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", + "2024-12-04 01:03:05,667 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", + "2024-12-04 01:03:05,667 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "figure plotting start\n", + "metrics: ['BiasPrLatRmse', 'BiasPrLonRmse', 'BiasSstLonRmse', 'BiasTauxLonRmse', 'EnsoAmpl', 'EnsoDuration', 'EnsoSeasonality', 'EnsoSstDiversity_2', 'EnsoSstLonRmse', 'EnsoSstSkew', 'EnsoSstTsRmse', 'SeasonalPrLatRmse', 'SeasonalPrLonRmse', 'SeasonalSstLonRmse', 'SeasonalTauxLonRmse']\n", + "filename_js: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "met: BiasPrLatRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_BiasPrLatRmse.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLatRmse\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: BiasPrLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_BiasPrLonRmse.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLonRmse\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: BiasSstLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_BiasSstLonRmse.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasSstLonRmse\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: BiasTauxLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_BiasTauxLonRmse.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasTauxLonRmse\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoAmpl\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoAmpl\n", + " dot 01:03\n", + " took 0 minute(s)\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoDuration\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoDuration.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoDuration\n", + " dot 01:03\n", + " took 0 minute(s)\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " boxplot 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoSeasonality\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoSeasonality\n", + " dot 01:03\n", + " took 0 minute(s)\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " hovmoeller 01:03\n", + " took 0 minute(s)\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoSstDiversity_2\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstDiversity_2.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoSstDiversity_2\n", + " dot 01:03\n", + " took 0 minute(s)\n", + " boxplot 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoSstLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n", + "met: EnsoSstSkew\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstSkew.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_EnsoSstSkew\n", + " dot 01:03\n", + " took 0 minute(s)\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoSstTsRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstTsRmse.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstTsRmse.nc\n", + "met: SeasonalPrLatRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_SeasonalPrLatRmse.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_SeasonalPrLatRmse\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + " hovmoeller 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: SeasonalPrLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_SeasonalPrLonRmse.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_SeasonalPrLonRmse\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + " hovmoeller 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: SeasonalSstLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_SeasonalSstLonRmse.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_SeasonalSstLonRmse\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + " hovmoeller 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: SeasonalTauxLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_SeasonalTauxLonRmse.nc\n", + "figure_name: cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_SeasonalTauxLonRmse\n", + " curve 01:03\n", + " took 0 minute(s)\n", + " map 01:03\n", + " took 0 minute(s)\n", + " hovmoeller 01:03\n", + " took 0 minute(s)\n", + "figure plotting done\n", "PMPdriver: model loop end\n", - "Process end: Tue Jul 2 14:42:29 2024\n" + "Process end: Wed Dec 4 01:03:28 2024\n" ] } ], @@ -680,16 +871,66 @@ "metadata": {}, "source": [ "This run saved metrics to two files: \n", - "basicTestEnso/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json basicTestEnso/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", + "- `basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json` \n", + "- `basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json`\n", "\n", "diveDown metrics are not available in all cases. \n", "\n", + "Example dive down (i.e., diagnostics) figures:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "af28cd74", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# figure size in inches optional\n", + "rcParams['figure.figsize'] = 12, 10\n", + "\n", + "# path to images\n", + "plot1 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLatRmse_diagnostic_divedown01.png\")\n", + "plot2 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_perf/cmip5_historical_ENSO_perf_ACCESS1-0_r1i1p1_BiasPrLatRmse_diagnostic_divedown02.png\")\n", + "\n", + "# display images\n", + "fig, ax = plt.subplots(1,2); ax[0].axis('off'); ax[1].axis('off')\n", + "ax[0].imshow(mpimg.imread(plot1))\n", + "ax[1].imshow(mpimg.imread(plot2))" + ] + }, + { + "cell_type": "markdown", + "id": "b5d97b22", + "metadata": {}, + "source": [ "The results section of cmip5_historical_ENSO_perf_basicTestEnso_ACCESS1-0_r1i1p1.json is shown below." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "511a1809", "metadata": {}, "outputs": [ @@ -715,11 +956,11 @@ " },\n", " \"metric\": {\n", " \"ERA-Interim\": {\n", - " \"value\": 1.1075304623846065,\n", + " \"value\": 1.107530462384606,\n", " \"value_error\": null\n", " },\n", " \"GPCPv2.3\": {\n", - " \"value\": 1.9301242972172135,\n", + " \"value\": 1.9301242972172137,\n", " \"value_error\": null\n", " }\n", " }\n", @@ -741,11 +982,11 @@ " },\n", " \"metric\": {\n", " \"ERA-Interim\": {\n", - " \"value\": 0.6464917771721344,\n", + " \"value\": 0.6464917771721341,\n", " \"value_error\": null\n", " },\n", " \"GPCPv2.3\": {\n", - " \"value\": 1.4165839641155156,\n", + " \"value\": 1.4165839641155153,\n", " \"value_error\": null\n", " }\n", " }\n", @@ -767,11 +1008,11 @@ " },\n", " \"metric\": {\n", " \"ERA-Interim\": {\n", - " \"value\": 0.6404287493308193,\n", + " \"value\": 0.6404292962163499,\n", " \"value_error\": null\n", " },\n", " \"HadISST\": {\n", - " \"value\": 0.49054786718298193,\n", + " \"value\": 0.49054878363003207,\n", " \"value_error\": null\n", " }\n", " }\n", @@ -789,7 +1030,7 @@ " },\n", " \"metric\": {\n", " \"ERA-Interim\": {\n", - " \"value\": 5.8373124987935965,\n", + " \"value\": 5.837312370331827,\n", " \"value_error\": null\n", " }\n", " }\n", @@ -805,8 +1046,8 @@ " \"value_error\": 0.1423236985494241\n", " },\n", " \"HadISST\": {\n", - " \"value\": 0.7688706055408969,\n", - " \"value_error\": 0.06298833428079066\n", + " \"value\": 0.7688706055408968,\n", + " \"value_error\": 0.06298833428079065\n", " }\n", " },\n", " \"metric\": {\n", @@ -815,8 +1056,8 @@ " \"value_error\": 17.543852271121864\n", " },\n", " \"HadISST\": {\n", - " \"value\": 13.766234667168376,\n", - " \"value_error\": 13.968772137924121\n", + " \"value\": 13.766234667168362,\n", + " \"value_error\": 13.968772137924118\n", " }\n", " }\n", " },\n", @@ -849,26 +1090,26 @@ " \"EnsoSeasonality\": {\n", " \"diagnostic\": {\n", " \"ACCESS1-0_r1i1p1\": {\n", - " \"value\": 1.6580607964897247,\n", - " \"value_error\": 0.26592975514284783\n", + " \"value\": 1.6580607585263336,\n", + " \"value_error\": 0.2659297490540511\n", " },\n", " \"ERA-Interim\": {\n", - " \"value\": 2.052960042006758,\n", - " \"value_error\": 0.6533381852649718\n", + " \"value\": 2.052959982219765,\n", + " \"value_error\": 0.6533381662382373\n", " },\n", " \"HadISST\": {\n", - " \"value\": 1.666626760243124,\n", - " \"value_error\": 0.27353125995822913\n", + " \"value\": 1.6666267556930234,\n", + " \"value_error\": 0.27353125921145444\n", " }\n", " },\n", " \"metric\": {\n", " \"ERA-Interim\": {\n", - " \"value\": 19.235603101705838,\n", - " \"value_error\": 38.65610552276642\n", + " \"value\": 19.235602598860513,\n", + " \"value_error\": 38.65610576344229\n", " },\n", " \"HadISST\": {\n", - " \"value\": 0.5139701316298112,\n", - " \"value_error\": 32.284081772797805\n", + " \"value\": 0.5139721378784582,\n", + " \"value_error\": 32.284081121752685\n", " }\n", " }\n", " },\n", @@ -898,81 +1139,29 @@ " }\n", " }\n", " },\n", - " \"EnsoSstLonRmse\": {\n", - " \"diagnostic\": {\n", - " \"ACCESS1-0_r1i1p1\": {\n", - " \"value\": null,\n", - " \"value_error\": null\n", - " },\n", - " \"ERA-Interim\": {\n", - " \"value\": null,\n", - " \"value_error\": null\n", - " },\n", - " \"HadISST\": {\n", - " \"value\": null,\n", - " \"value_error\": null\n", - " }\n", - " },\n", - " \"metric\": {\n", - " \"ERA-Interim\": {\n", - " \"value\": 0.16401033023030476,\n", - " \"value_error\": null\n", - " },\n", - " \"HadISST\": {\n", - " \"value\": 0.14620423327573992,\n", - " \"value_error\": null\n", - " }\n", - " }\n", - " },\n", " \"EnsoSstSkew\": {\n", " \"diagnostic\": {\n", " \"ACCESS1-0_r1i1p1\": {\n", - " \"value\": -0.3339196537261687,\n", - " \"value_error\": -0.02673496883520269\n", + " \"value\": -0.3339196537261717,\n", + " \"value_error\": -0.02673496883520293\n", " },\n", " \"ERA-Interim\": {\n", - " \"value\": 0.40501535626049495,\n", - " \"value_error\": 0.06403855065638503\n", + " \"value\": 0.4050153562604903,\n", + " \"value_error\": 0.06403855065638428\n", " },\n", " \"HadISST\": {\n", - " \"value\": 0.40320728014992363,\n", - " \"value_error\": 0.033032027448448076\n", + " \"value\": 0.403207280149916,\n", + " \"value_error\": 0.03303202744844745\n", " }\n", " },\n", " \"metric\": {\n", " \"ERA-Interim\": {\n", - " \"value\": 182.4461711302128,\n", - " \"value_error\": -19.63686084226418\n", + " \"value\": 182.4461711302145,\n", + " \"value_error\": -19.636860842264586\n", " },\n", " \"HadISST\": {\n", - " \"value\": 182.81587911855365,\n", - " \"value_error\": -13.415118084476173\n", - " }\n", - " }\n", - " },\n", - " \"EnsoSstTsRmse\": {\n", - " \"diagnostic\": {\n", - " \"ACCESS1-0_r1i1p1\": {\n", - " \"value\": null,\n", - " \"value_error\": null\n", - " },\n", - " \"ERA-Interim\": {\n", - " \"value\": null,\n", - " \"value_error\": null\n", - " },\n", - " \"HadISST\": {\n", - " \"value\": null,\n", - " \"value_error\": null\n", - " }\n", - " },\n", - " \"metric\": {\n", - " \"ERA-Interim\": {\n", - " \"value\": 0.10604060446246283,\n", - " \"value_error\": null\n", - " },\n", - " \"HadISST\": {\n", - " \"value\": 0.07377326537861505,\n", - " \"value_error\": null\n", + " \"value\": 182.81587911855596,\n", + " \"value_error\": -13.415118084476546\n", " }\n", " }\n", " },\n", @@ -993,11 +1182,11 @@ " },\n", " \"metric\": {\n", " \"ERA-Interim\": {\n", - " \"value\": 1.1594264845137487,\n", + " \"value\": 1.1594264845137485,\n", " \"value_error\": null\n", " },\n", " \"GPCPv2.3\": {\n", - " \"value\": 1.5589152451460864,\n", + " \"value\": 1.5589152451460861,\n", " \"value_error\": null\n", " }\n", " }\n", @@ -1045,11 +1234,11 @@ " },\n", " \"metric\": {\n", " \"ERA-Interim\": {\n", - " \"value\": 0.28801464882030564,\n", + " \"value\": 0.2880146575579485,\n", " \"value_error\": null\n", " },\n", " \"HadISST\": {\n", - " \"value\": 0.3080045183114524,\n", + " \"value\": 0.308004517035873,\n", " \"value_error\": null\n", " }\n", " }\n", @@ -1067,7 +1256,7 @@ " },\n", " \"metric\": {\n", " \"ERA-Interim\": {\n", - " \"value\": 4.234563898790571,\n", + " \"value\": 4.234563900263323,\n", " \"value_error\": null\n", " }\n", " }\n", @@ -1103,19 +1292,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "6629c5d8", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ordonez4/miniconda3/envs/pmp_test/lib/python3.10/site-packages/cdms2/MV2.py:318: Warning: arguments order for compress function has changed\n", - "it is now: MV2.copmress(array,condition), if your code seems to not react or act wrong to a call to compress, please check this\n", - " warnings.warn(\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -1130,14 +1310,14 @@ "debug: False\n", "obs_cmor: True\n", "obs_cmor_path: demo_data/obs4MIPs_PCMDI_monthly\n", - "egg_pth: /home/ordonez4/miniconda3/envs/pmp_test/share/pmp\n", + "egg_pth: /Users/lee1043/mambaforge/envs/pmp_devel_20241202/share/pmp\n", "output directory for graphics:demo_output/basicTestEnso/ENSO_tel\n", "output directory for diagnostic_results:demo_output/basicTestEnso/ENSO_tel\n", "output directory for metrics_results:demo_output/basicTestEnso/ENSO_tel\n", "list_variables: ['pr', 'sst']\n", "list_obs: ['AVISO-1-0', 'ERA-INT', 'GPCP-2-3', 'HadISST-1-1']\n", "PMPdriver: dict_obs readin end\n", - "Process start: Tue Jul 2 14:42:40 2024\n", + "Process start:Wed Dec 4 01:03:41 2024\n", "models: ['ACCESS1-0']\n", " ----- model: ACCESS1-0 ---------------------\n", "PMPdriver: var loop start for model ACCESS1-0\n", @@ -1242,8 +1422,8 @@ "\u001b[93m\n", "\u001b[93m\n", " %%%%% ----- %%%%%\n", - " ERROR File /home/ordonez4/miniconda3/envs/pmp_test/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1208, in CheckUnits: units\n", - " the file says that temperature (ts) is in K but it seems unlikely ([-1e+30, 304.7203])\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", " %%%%% ----- %%%%%\n", "\u001b[0m\n", "\u001b[0m\n", @@ -1253,54 +1433,12 @@ "\u001b[94m ComputeMetric: twoVarRMSmetric, EnsoPrMapDjf = ACCESS1-0_r1i1p1 and ERA-Interim_ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: twoVarRMSmetric, EnsoPrMapDjf = ACCESS1-0_r1i1p1 and ERA-Interim_GPCPv2.3\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: twoVarRMSmetric, EnsoPrMapDjf = ACCESS1-0_r1i1p1 and HadISST_ERA-Interim\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: twoVarRMSmetric, EnsoPrMapDjf = ACCESS1-0_r1i1p1 and HadISST_GPCPv2.3\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "operands could not be broadcast together with shapes (120,360) (0,) \n", "\u001b[94m ComputeCollection: metric = EnsoPrMapJja\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarRMSmetric, EnsoPrMapJja = ACCESS1-0_r1i1p1 and ERA-Interim_ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: twoVarRMSmetric, EnsoPrMapJja = ACCESS1-0_r1i1p1 and ERA-Interim_GPCPv2.3\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: twoVarRMSmetric, EnsoPrMapJja = ACCESS1-0_r1i1p1 and HadISST_ERA-Interim\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO::2024-07-02 14:58::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n", - "2024-07-02 14:58:27,009 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n", - "2024-07-02 14:58:27,009 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n", - "INFO::2024-07-02 14:58::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", - "2024-07-02 14:58:39,971 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", - "2024-07-02 14:58:39,971 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[94m ComputeMetric: twoVarRMSmetric, EnsoPrMapJja = ACCESS1-0_r1i1p1 and HadISST_GPCPv2.3\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "operands could not be broadcast together with shapes (120,360) (0,) \n", "\u001b[94m ComputeCollection: metric = EnsoSeasonality\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", @@ -1318,8 +1456,8 @@ "\u001b[93m\n", "\u001b[93m\n", " %%%%% ----- %%%%%\n", - " ERROR File /home/ordonez4/miniconda3/envs/pmp_test/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1208, in CheckUnits: units\n", - " the file says that temperature (ts) is in K but it seems unlikely ([-1e+30, 304.7203])\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", " %%%%% ----- %%%%%\n", "\u001b[0m\n", "\u001b[0m\n", @@ -1329,35 +1467,79 @@ "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[93m\n", - "\u001b[93m\n", - " %%%%% ----- %%%%%\n", - " ERROR File /home/ordonez4/miniconda3/envs/pmp_test/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1208, in CheckUnits: units\n", - " the file says that temperature (ts) is in K but it seems unlikely ([-1e+30, 304.7203])\n", - " %%%%% ----- %%%%%\n", - "\u001b[0m\n", - "\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "operands could not be broadcast together with shapes (10,120) (0,) \n", "\u001b[94m ComputeCollection: metric = EnsoSstMapDjf\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstMapDjf = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "operands could not be broadcast together with shapes (120,360) (0,) \n", "\u001b[94m ComputeCollection: metric = EnsoSstMapJja\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstMapJja = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "operands could not be broadcast together with shapes (120,360) (0,) \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO::2024-12-04 01:07::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "2024-12-04 01:07:24,112 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "2024-12-04 01:07:24,112 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "INFO::2024-12-04 01:07::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", + "2024-12-04 01:07:35,543 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", + "2024-12-04 01:07:35,543 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "figure plotting start\n", + "metrics: ['EnsoAmpl', 'EnsoPrMapDjf', 'EnsoPrMapJja', 'EnsoSeasonality', 'EnsoSstLonRmse', 'EnsoSstMapDjf', 'EnsoSstMapJja']\n", + "filename_js: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "met: EnsoAmpl\n", + "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n", + "figure_name: cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl\n", + " dot 01:07\n", + " took 0 minute(s)\n", + " curve 01:07\n", + " took 0 minute(s)\n", + " map 01:07\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoPrMapDjf\n", + "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoPrMapDjf.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoPrMapDjf.nc\n", + "met: EnsoPrMapJja\n", + "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoPrMapJja.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoPrMapJja.nc\n", + "met: EnsoSeasonality\n", + "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\n", + "figure_name: cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoSeasonality\n", + " dot 01:07\n", + " took 0 minute(s)\n", + " curve 01:07\n", + " took 0 minute(s)\n", + " hovmoeller 01:07\n", + " took 0 minute(s)\n", + " curve 01:07\n", + " took 0 minute(s)\n", + " map 01:07\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoSstLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n", + "met: EnsoSstMapDjf\n", + "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstMapDjf.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstMapDjf.nc\n", + "met: EnsoSstMapJja\n", + "filename_nc: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstMapJja.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstMapJja.nc\n", "PMPdriver: model loop end\n", - "Process end: Tue Jul 2 14:58:39 2024\n" + "Process end: Wed Dec 4 01:07:42 2024\n" ] } ], @@ -1379,7 +1561,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "f963d430", "metadata": {}, "outputs": [ @@ -1387,13 +1569,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\r\n", - "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoPrMapDjf.nc\r\n", - "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoPrMapJja.nc\r\n", - "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\r\n", - "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\r\n", - "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstMapDjf.nc\r\n", - "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstMapJja.nc\r\n" + "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n", + "demo_output/basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\n" ] } ], @@ -1401,6 +1578,49 @@ "!ls {demo_output_directory + \"/basicTestEnso/ENSO_tel/*.nc\"}" ] }, + { + "cell_type": "code", + "execution_count": 13, + "id": "46bfec14", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# figure size in inches optional\n", + "rcParams['figure.figsize'] = 16, 10\n", + "\n", + "# path to images\n", + "plot1 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl_diagnostic_divedown01.png\")\n", + "plot2 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl_diagnostic_divedown02.png\")\n", + "plot3 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_tel/cmip5_historical_ENSO_tel_ACCESS1-0_r1i1p1_EnsoAmpl_diagnostic_divedown03.png\")\n", + "\n", + "# display images\n", + "fig, ax = plt.subplots(1,3); ax[0].axis('off'); ax[1].axis('off'); ax[2].axis('off')\n", + "ax[0].imshow(mpimg.imread(plot1))\n", + "ax[1].imshow(mpimg.imread(plot2))\n", + "ax[2].imshow(mpimg.imread(plot3))" + ] + }, { "cell_type": "markdown", "id": "c48822c8", @@ -1411,7 +1631,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "71ad2751", "metadata": {}, "outputs": [ @@ -1429,7 +1649,7 @@ "debug: False\n", "obs_cmor: True\n", "obs_cmor_path: demo_data/obs4MIPs_PCMDI_monthly\n", - "egg_pth: /home/ordonez4/miniconda3/envs/pmp_test/share/pmp\n", + "egg_pth: /Users/lee1043/mambaforge/envs/pmp_devel_20241202/share/pmp\n", "output directory for graphics:demo_output/basicTestEnso/ENSO_proc\n", "output directory for diagnostic_results:demo_output/basicTestEnso/ENSO_proc\n", "output directory for metrics_results:demo_output/basicTestEnso/ENSO_proc\n", @@ -1439,7 +1659,7 @@ "\u001b[95mObservation dataset GPCP-2-3 is not given for variable thf\u001b[0m\n", "\u001b[95mObservation dataset HadISST-1-1 is not given for variable thf\u001b[0m\n", "PMPdriver: dict_obs readin end\n", - "Process start: Tue Jul 2 14:58:50 2024\n", + "Process start:Wed Dec 4 01:07:56 2024\n", "models: ['ACCESS1-0']\n", " ----- model: ACCESS1-0 ---------------------\n", "PMPdriver: var loop start for model ACCESS1-0\n", @@ -1565,13 +1785,7 @@ " \"demo_data/CMIP5_demo_data/hfss_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n", " \"demo_data/CMIP5_demo_data/rlds_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n", " \"demo_data/CMIP5_demo_data/rlus_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n", - " \"demo_data/CMIP5_demo_data/rsds_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " \"demo_data/CMIP5_demo_data/rsds_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\",\n", " \"demo_data/CMIP5_demo_data/rsus_Amon_ACCESS1-0_historical_r1i1p1_185001-200512.nc\"\n", " ],\n", " \"path + filename_area\": [\n", @@ -1693,26 +1907,21 @@ "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, BiasSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[94m ComputeCollection: metric = BiasTauxLonRmse\u001b[0m\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ordonez4/miniconda3/envs/pmp_test/lib/python3.10/site-packages/cdms2/MV2.py:318: Warning: arguments order for compress function has changed\n", - "it is now: MV2.copmress(array,condition), if your code seems to not react or act wrong to a call to compress, please check this\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\u001b[93m\n", + "\u001b[93m\n", + " %%%%% ----- %%%%%\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", + " %%%%% ----- %%%%%\n", + "\u001b[0m\n", + "\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[94m ComputeCollection: metric = BiasTauxLonRmse\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, BiasTauxLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", @@ -1720,12 +1929,30 @@ "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeCollection: metric = EnsoAmpl\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m\n", + "\u001b[93m\n", + " %%%%% ----- %%%%%\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", + " %%%%% ----- %%%%%\n", + "\u001b[0m\n", + "\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeCollection: metric = EnsodSstOce_2\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", @@ -1735,7 +1962,32 @@ "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", "\u001b[93m hfss sign reversed\u001b[0m\n", "\u001b[93m range old = -3.28 to +31.07\u001b[0m\n", - "\u001b[93m range new = -31.07 to +3.28\u001b[0m\n", + "\u001b[93m range new = -31.07 to +3.28\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/cdms2/MV2.py:318: Warning: arguments order for compress function has changed\n", + "it is now: MV2.copmress(array,condition), if your code seems to not react or act wrong to a call to compress, please check this\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfls sign reversed\u001b[0m\n", + "\u001b[93m range old = +1.99 to +290.82\u001b[0m\n", + "\u001b[93m range new = -290.82 to -1.99\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfss sign reversed\u001b[0m\n", + "\u001b[93m range old = -3.28 to +62.31\u001b[0m\n", + "\u001b[93m range new = -62.31 to +3.28\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarmetric = ERA-Interim_ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", @@ -1753,6 +2005,23 @@ "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfls sign reversed\u001b[0m\n", + "\u001b[93m range old = +3.32 to +243.35\u001b[0m\n", + "\u001b[93m range new = -243.35 to -3.32\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfss sign reversed\u001b[0m\n", + "\u001b[93m range old = -9.37 to +72.85\u001b[0m\n", + "\u001b[93m range new = -72.85 to +9.37\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarmetric = HadISST_ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", @@ -1770,6 +2039,23 @@ "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfls sign reversed\u001b[0m\n", + "\u001b[93m range old = +3.32 to +243.35\u001b[0m\n", + "\u001b[93m range new = -243.35 to -3.32\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfss sign reversed\u001b[0m\n", + "\u001b[93m range old = -9.37 to +72.85\u001b[0m\n", + "\u001b[93m range new = -72.85 to +9.37\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeCollection: metric = EnsoFbSshSst\u001b[0m\n", "\u001b[94m ComputeCollection: ENSO_proc, metric EnsoFbSshSst not computed\u001b[0m\n", "\u001b[94m reason(s):\u001b[0m\n", @@ -1777,14 +2063,23 @@ "\u001b[94m ComputeCollection: metric = EnsoFbSstTaux\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarmetric = ERA-Interim_ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarmetric = HadISST_ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageHorizontal\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeCollection: metric = EnsoFbSstThf\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", @@ -1795,6 +2090,16 @@ "\u001b[93m hfss sign reversed\u001b[0m\n", "\u001b[93m range old = -3.28 to +31.07\u001b[0m\n", "\u001b[93m range new = -31.07 to +3.28\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfls sign reversed\u001b[0m\n", + "\u001b[93m range old = +1.99 to +290.82\u001b[0m\n", + "\u001b[93m range new = -290.82 to -1.99\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfss sign reversed\u001b[0m\n", + "\u001b[93m range old = -3.28 to +62.31\u001b[0m\n", + "\u001b[93m range new = -62.31 to +3.28\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarmetric = ERA-Interim_ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", @@ -1811,6 +2116,23 @@ "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfls sign reversed\u001b[0m\n", + "\u001b[93m range old = +3.32 to +243.35\u001b[0m\n", + "\u001b[93m range new = -243.35 to -3.32\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfss sign reversed\u001b[0m\n", + "\u001b[93m range old = -9.37 to +72.85\u001b[0m\n", + "\u001b[93m range new = -72.85 to +9.37\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: twoVarmetric = HadISST_ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", @@ -1827,53 +2149,194 @@ "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfls sign reversed\u001b[0m\n", + "\u001b[93m range old = +3.32 to +243.35\u001b[0m\n", + "\u001b[93m range new = -243.35 to -3.32\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib ReadAndSelectRegion\u001b[0m\n", + "\u001b[93m hfss sign reversed\u001b[0m\n", + "\u001b[93m range old = -9.37 to +72.85\u001b[0m\n", + "\u001b[93m range new = -72.85 to +9.37\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeCollection: metric = EnsoFbTauxSsh\u001b[0m\n", "\u001b[94m ComputeCollection: ENSO_proc, metric EnsoFbTauxSsh not computed\u001b[0m\n", "\u001b[94m reason(s):\u001b[0m\n", "\u001b[94m no modeled ssh given\u001b[0m\n", "\u001b[94m ComputeCollection: metric = EnsoSeasonality\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", + "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m\n", + "\u001b[93m\n", + " %%%%% ----- %%%%%\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", + " %%%%% ----- %%%%%\n", + "\u001b[0m\n", + "\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[94m ComputeCollection: metric = EnsoSstLonRmse\u001b[0m\n", "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and ERA-Interim\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "operands could not be broadcast together with shapes (10,120) (0,) \n", + "\u001b[94m ComputeCollection: metric = EnsoSstSkew\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarRMSmetric, EnsoSstLonRmse = ACCESS1-0_r1i1p1 and HadISST\u001b[0m\n", + "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[93m EnsoUvcdatToolsLib AverageMeridional\u001b[0m\n", - "\u001b[94m ComputeCollection: metric = EnsoSstSkew\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = ACCESS1-0_r1i1p1\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = ERA-Interim\u001b[0m\n" + "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m\n", + "\u001b[93m\n", + " %%%%% ----- %%%%%\n", + " ERROR File /Users/lee1043/mambaforge/envs/pmp_devel_20241202/lib/python3.10/site-packages/EnsoMetrics/EnsoUvcdatToolsLib.py, line 1215, in CheckUnits: units\n", + " the file says that temperature (ts) is in K but it seems unlikely ([np.float32(-1e+30), np.float32(304.7203)])\n", + " %%%%% ----- %%%%%\n", + "\u001b[0m\n", + "\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO::2024-07-02 15:04::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n", - "2024-07-02 15:04:16,643 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n", - "2024-07-02 15:04:16,643 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n", - "INFO::2024-07-02 15:04::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", - "2024-07-02 15:04:29,807 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", - "2024-07-02 15:04:29,807 [INFO]: base.py(write:443) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n" + "INFO::2024-12-04 01:16::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "2024-12-04 01:16:41,607 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "2024-12-04 01:16:41,607 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "INFO::2024-12-04 01:16::pcmdi_metrics:: Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", + "2024-12-04 01:16:52,492 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n", + "2024-12-04 01:16:52,492 [INFO]: base.py(write:422) >> Results saved to a json file: /Users/lee1043/Documents/Research/git/pcmdi_metrics_20230620_pcmdi/pcmdi_metrics/doc/jupyter/Demo/demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_diveDown.json\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", - "\u001b[94m ComputeMetric: oneVarmetric = HadISST\u001b[0m\n", - "\u001b[93m NOTE: Estimated landmask applied\u001b[0m\n", + "figure plotting start\n", + "metrics: ['BiasSstLonRmse', 'BiasTauxLonRmse', 'EnsoAmpl', 'EnsodSstOce_2', 'EnsoFbSshSst', 'EnsoFbSstTaux', 'EnsoFbSstThf', 'EnsoFbTauxSsh', 'EnsoSeasonality', 'EnsoSstLonRmse', 'EnsoSstSkew']\n", + "filename_js: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1.json\n", + "met: BiasSstLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_BiasSstLonRmse.nc\n", + "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_BiasSstLonRmse\n", + " curve 01:16\n", + " took 0 minute(s)\n", + " map 01:16\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: BiasTauxLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_BiasTauxLonRmse.nc\n", + "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_BiasTauxLonRmse\n", + " curve 01:16\n", + " took 0 minute(s)\n", + " map 01:16\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoAmpl\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoAmpl.nc\n", + "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoAmpl\n", + " dot 01:16\n", + " took 0 minute(s)\n", + " curve 01:16\n", + " took 0 minute(s)\n", + " map 01:16\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsodSstOce_2\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsodSstOce_2.nc\n", + "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsodSstOce_2\n", + " dot 01:16\n", + " took 0 minute(s)\n", + " curve 01:16\n", + " took 0 minute(s)\n", + " curve 01:16\n", + " took 0 minute(s)\n", + " hovmoeller 01:16\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoFbSshSst\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbSshSst.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbSshSst.nc\n", + "met: EnsoFbSstTaux\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbSstTaux.nc\n", + "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoFbSstTaux\n", + " scatterplot 01:17\n", + " took 0 minute(s)\n", + " scatterplot 01:17\n", + " took 0 minute(s)\n", + " curve 01:17\n", + " took 0 minute(s)\n", + " hovmoeller 01:17\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoFbSstThf\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbSstThf.nc\n", + "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoFbSstThf\n", + " scatterplot 01:17\n", + " took 0 minute(s)\n", + " scatterplot 01:17\n", + " took 0 minute(s)\n", + " curve 01:17\n", + " took 0 minute(s)\n", + " hovmoeller 01:17\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoFbTauxSsh\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbTauxSsh.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoFbTauxSsh.nc\n", + "met: EnsoSeasonality\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSeasonality.nc\n", + "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoSeasonality\n", + " dot 01:17\n", + " took 0 minute(s)\n", + " curve 01:17\n", + " took 0 minute(s)\n", + " hovmoeller 01:17\n", + " took 0 minute(s)\n", + " curve 01:17\n", + " took 0 minute(s)\n", + " map 01:17\n", + " took 0 minute(s)\n", + "figure plotting done\n", + "met: EnsoSstLonRmse\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n", + "file not found: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstLonRmse.nc\n", + "met: EnsoSstSkew\n", + "filename_nc: demo_output/basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_basicTestEnso_ACCESS1-0_r1i1p1_EnsoSstSkew.nc\n", + "figure_name: cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoSstSkew\n", + " dot 01:17\n", + " took 0 minute(s)\n", + " curve 01:17\n", + " took 0 minute(s)\n", + " map 01:17\n", + " took 0 minute(s)\n", + "figure plotting done\n", "PMPdriver: model loop end\n", - "Process end: Tue Jul 2 15:04:29 2024\n" + "Process end: Wed Dec 4 01:17:08 2024\n" ] } ], @@ -1883,13 +2346,56 @@ "--metricsCollection ENSO_proc \\\n", "--results_dir $1/basicTestEnso/ENSO_proc" ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e91aaa1e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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gwADy8/Pp0aMHDzzwAEuXLuXZZ58lNTW13Mp6N1JYWMj+/fvJy8u7s4MiCIIg/OsKCws5d+4ckiSxYcMGSkpKKtzmgw8+IDMzky5duvDLL7/w008/3YXeCoIgCIIgCIJwt1TLDDsASZLo0qULYWFh5Z4zGo0AtG/fnu+//57HHnuMmjVrlttuxYoVhISEMGnSJLy9vZXXWgN8CQkJTJgwgSeeeIIxY8bg4OCALMu0b9+eoUOHMmvWLCWj70qyLFNYWEhMTIxS0NrX1xc/P79yfZBlmTNnzlBaWkrdunUBSExMJDExEYDAwEB8fHyQJIkLFy4gyzKhoaEAxMTEkJeXR7169dDpdKSlpZGYmEhUVBRpaWlkZWXh4+PDpUuXsFgsBAcH22QFXllUXKfTERoaiqurq1LcOzk5mbi4OMxmM05OTgQHB+Po6IjZbCY2NpaUlBQA3NzcCA4OtllhUBAE4X7k4eHBxIkTOXr0KPv3769wm4sXL3Lq1CmWL1+Oh4cHpaWlLFiwgIEDB2Jvb/8f91gQBEEQBEEQhLuh2gbsKqNnz57Mnz+f1atXM3z48HLPm0wm8vLyyM/Px93dHbVarQSdZFlm27ZtmM1mBg4cqEx5kiSJiIgI+vbtyy+//MIrr7xCYGCgTbv5+flMmDCBbdu24eTkRHFxMfXq1WPBggU225nNZrZt28ZHH33EwIEDiYyM5M8//2TSpEkUFxcjyzKenp5MnTqVRo0asWLFCvbt28dPP/1ESUkJb7/9NqdOnWLNmjVERkayePFidu7cyffff8+qVatYsmQJtWvX5vLlyyQnJxMREcHChQvx9PTEbDbz7bffsnDhQlQqlbLq3qeffkqNGjU4fPgw7733HhkZGRgMBkpLSxkzZgy9evVi/fr1TJ8+HYvFglqtRqfTMWPGDBo1avTv7EhBEIR7RGXKIGRlZWFnZ4eDgwOSJFG7dm3y8vJIT09Xfi8sFotyc8lkMiFJEmq1+l/tuyDcy0wmE/b29mKlXkEQBEEQ7hvVNmAnyzJr165VMuMAgoODadmypfJ3b29vhg4dyvfff0/Pnj3LtfH444+zfv16unfvTrt27WjevDktW7ZUMt2io6NxdnYmJCTE5nWSJNGkSRNmzZpFRkZGuYBdXFwcy5cvZ82aNYSFhWE2m0lLS7PZxmQysXLlSqZNm8aoUaPo378/ly5dYvz48QwaNIinnnoKWZaZPHky48ePZ/ny5bRo0YIFCxaQn59PYWEhly5dwmAwEB0dTe3atdmzZw9NmzbF0dERgHPnzvHiiy/y+OOPc+nSJZ577jl+++03nn32WXbu3Mm8efOYOHEiDz/8MNnZ2YwYMYI5c+YwceJEVqxYgZeXF8uWLUOtVlNUVITJZCI/P59FixbRu3dvXnnlFSRJIjc3V9RmEgRB+P8sFguSJCnBPWsAwmKxKNucOXOG2bNnU1BQQHJyMlqtFhcXFyRJQqVSIcuysr01mGc2m5UyDNbgnjUj3LqNxWJRXqdSqZAkCYvFgizLN9329V6nUqlQqVSYzWalD1qt9o63feXr7oUxuVbb1n2u0WjueNv/5b68nbYtFovS1s3uS5VKRWJiIkuXLsXX1xdBEARBEIT7QbUO2P35559KcAqgdevWNGvWTPm7SqXi8ccf57vvvmPVqlV069bNpo02bdrw008/sW7dOnbs2MH69etxdnZm0qRJ9OzZk5KSEtRqdbkprwAGgwGTyYTJZKrwOY1Gw+7du3F0dMTDw4OQkBDl4s1isbBs2TL27NnDu+++S48ePVCr1Rw8eJCsrCyllh5AixYt2Lp1K4mJidSsWRNXV1f++ecfZFnGy8uL8PBwjh49SqNGjUhOTqZx48bKiX1kZCSdO3fGycmJiIgIwsPDOXv2LGazmS1btuDt7U1kZKRS069169Zs2rSJgoIC3N3d2b17N0ePHqV27dq4urpiZ2dHYWEh7u7uHD16lLNnzxIQEICHhwc6ne72d6ogCMJ9wNHRkeLiYkwmE7Isk5qaik6nw9XVVdkmMjKS2bNnI8sy8+fPJyAggI4dOwKg0WiQZdkmgKPVaiktLVWCHhpN2c+/9TfIGiwym802wRK1Wk1paanyvneqbbVajUqlorS0lNTUVIxGI8HBweXa1mg0yjjcTr+1Wi0Wi+W6bavVaiRJsvld1mq11+z37bat0+kwmUxYLBZSU1MpLS0lKCjIZpvKjPfVbd/JfXmrbVdmTK7el6mpqZjNZvz8/JRtrmzbZDJRWFiIxWJBo9Gg1+vRarVKcHvkyJEiu04QBEEQhPtKtQ3YqVQqPvvsM6Wem5UkSco0IyirN2StN3dlMA/KTmSbN29Os2bNkGWZs2fPMnr0aGbNmsUDDzyAp6cnRUVFZGdn4+npqbxOlmWSkpJwcnKqsB5RzZo1mTZtGj/88ANz5szB39+fF154gYEDBwJldfJ++eUXWrVqRefOnZWT7tTUVGJjYxk8eLBNe+7u7hQXFxMQEEBISAi7d+/GYrHQpEkT2rRpw7x582jWrBkFBQU0btxY6aOrq6sylVer1aLX6ykpKaG0tJSUlBQOHTpE//79bT5XREQEJpOJwYMHk5eXx9ixY8nNzaVVq1aMHTuWWrVq8fbbbzNr1ixeeuklzGYzXbt2ZfTo0TZjJAiCcD+yWCwkJSWRnJxMcXExCQkJaDQaiouLOXr0KO3atSMoKAh3d3fWrVtH69at+fnnn2nZsiUuLi5KOyqVCp1OhyzLaDQaDAaDMoX2Wm71xkhl6oveats6nQ6NRkNpaSl6vb7C/t9qfdPKvO5ut33lPrSOgcFgqNTrbsW/uS9vZ0yuHAN7e3ub48BkMnHixAmWLFnCr7/+Sl5eHlqtlgEDBjB27Fhq1qyJ2WxWgouCIAiCIAj3i2obsIP/m0ZxI127duXHH39k6dKlyt1iwOYOMJRlPPTo0YN58+aRn59Py5YtWbRoEQcOHKBr167KiWRJSQl//PEH4eHh1KhRo9z76XQ6+vbtS4cOHcjMzGTp0qVMnTpVCRjq9XrGjh3LkiVL+PDDD3nvvfdwcXHBzc2NkJAQVqxYYXPCr1arcXd3R6PR0LBhQw4dOkRxcTFDhgyhVq1apKens2vXLnx8fPDx8bEZn4r+rNFocHd3p02bNnzxxRc2Y6jVanF1dUWtVvPuu+/y6quvEhsby7vvvstnn33GrFmzCA8PZ8aMGWRlZXHy5ElGjRqFv78/r732mjjZFgThvlZSUsIHH3xAbGwsOTk5jBkzhpdffpkaNWrw5Zdf0rJlSzw8PBg7dixz585l9erVBAUFMXTo0Pv2+7Gyv8X3MzEG5cegtLSUQ4cOMX/+fLZu3Up8fLzNtPD58+dz/vx55s6dW670iJX1PE2oeu7X7ztBEARBuBnVOmBXWlpKSUmJ8nfrdJCr+fv78+STTzJ+/HibE4iNGzfi5+dHeHg4arWa7Oxsdu7cSY0aNXB1daVmzZo0bdqUadOmERQURGhoKBaLhZ9++onNmzfz2Wef4ebmVu790tLSyMzMJDAwED8/P5o0acKyZcvIz8/HYDAgSRKNGjWiUaNGDBs2jE8//ZRx48bRtGlT7O3t2bJlC08++SQ6nY7i4mIuXLiAt7c3kiTRqlUrvvzySxwdHWncuDFeXl54eXmxePFiXn755UpdMGg0Gtq3b8+mTZv4559/ePjhh5Ekifz8fFJSUnB3d+fUqVN4e3vj6uqKvb09ISEhZGVlUVhYSGxsLCEhIXh4eNCkSRPc3NzIyspSat8IgiDcrwwGA/Pnz7d5zDqlb8WKFUpJgiZNmrBw4ULle/F+/m50d3evsDxEdSLGwHYMcnNzmTdvHp988gnp6ekVbm8ymdi8eTOTJk1i9uzZNs9ZA3UFBQUUFBSIwF0VolarcXJyumbGrSAIgiBUJ9U2YGexWHjllVdspqS6u7szc+bMclM2JEliwIABLFiwgHPnzimPx8TEMH78eHQ6HQaDgfT0dFxcXBg/fjweHh4ATJ8+nY8//pinn34aFxcXiouLUavVfPDBB3Tv3r3Ck5H4+HilbzqdjoyMDHr06EHdunWJiYlRtmvUqBHz589n5MiRGI1G3n//fT744AOmTZvGokWLcHBwoLCwkHr16vH1118rrwHw9PQkJCQEtVpNgwYN2LZtG61bt670+HXo0IEXXniBDz74QKkjU1xcTN++fQkPD2fVqlWsWbMGNzc3iouLsVgsfPDBBwDMnTuXEydO4OLiQm5uLu7u7jz++OPixEwQhPve9VZzvfLx+z1IdyVrnbLqTIzB/41BYmIi7733HsuWLaO4uFh53tHRkYYNGxISEsL27duJi4tDlmXWrVvH//73v3LtFRYWkpGRoWT9C1VDSUkJKSkp+Pv7K3UOBUEQBKG6kuRqeNuxoKCAXbt2lbvjqtfrad26tbLgQ506dfDy8lJWVjt16hTJyck0aNAAb29vJVssLS2N4uJi7OzsCAkJwc/Pz+ZCq7CwkOjoaDIyMtBqtQQGBhIYGHjNE8iSkhJiYmJIS0vDZDLh4eFBeHg4dnZ2FBQUcOzYMerXr4+zszOyLHPu3DmSkpJo2bIlWq2W+Ph4Ll26RGlpKS4uLgQHB+Pp6akUjv77779xcHCgYcOGSJLE5cuXuXDhAk2aNFFqJMXGxpKenk79+vWVrMMTJ06g1WqJjIwEyjIUL1y4QEJCAlAW8AwNDcXJyYmMjAwuXbpETk4OWq2WgIAAgoKCAEhKSiI2NpaCggLs7OwICwvDx8en2lycCoIg3CmyLDNv3jxCQkLo0qVLlfselWWZuLg4ioqKCA8Pr3L9vxOq8xjIsqys+Hrp0iUOHz7M999/z6ZNm5RsO51OR+fOnXnqqafo0qULrq6urF27loEDByqzJCZMmEB8fDyTJk3Cy8sLWZbJycmhtLRUOf+51vtbLBaMRiP5+fmoVCocHBzQ6XTVKmh+LzGbzSQlJeHl5XXLNSYFQRAE4X5RLQN2giAIgnA/EAG7qq+6joHJZGL37t0cOnSIzMxMDhw4wN9//01ubq5yQ9Xf35+xY8cyZMgQHB0dgbLs09zcXNq1a8eRI0cAaNq0KUFBQcybN88mYGe96VnRmMqyTF5eHitWrOD777/n2LFjqNVqWrRowdChQ+natSt2dnbVZn/cK8xmM8nJyXh6eoqAnSAIglDtiVxzQRAEQRDuGrVaXe2nvlWnMZBlmcLCQr7//nsmTZpEUlJSuW0kSSI4OJiZM2fSrVu3cvWFHRwc6NevH8eOHcNisXDu3DmysrJuqh/5+flMnjyZOXPmUFRUpDy+ceNG9u7dy3vvvceIESNuedXciphMJr777jv279/P//73P2XGgpUsy6Snp1NYWKg85u3tDZTVN5ZlGZ1Oh7u7uxLMMplMpKam4ubmhp2dXbn3zMjIwNHRscLgl8ViITMzE1dX10off3/99Rcmk4mOHTtW+nMLgiAIgnBr7umzw7y8PJYtW0ZMTAwNGzakd+/eNjXnAOVEbfXq1WRlZVG/fn369etX4UmLIAiCIAj3lopWS69uqssYyLKM0Whk8uTJzJw50yZQZqXT6ejatSvvvvsuzZo1q3AxLJVKRZcuXfj666+Jj4+3ycqrbD/27t3LzJkzMRqN5Z7Pzs5m8uTJdO7cmXr16lUqy+7K97dYLBX2OzY2lo0bNzJnzhwlEHelkpISXn/9dXQ6HW5ubkiSxMsvv0xcXBzvv/8+LVu2JCMjgzZt2vDSSy+hUqmIiYlh8ODBvPnmmzz55JPl+vr+++8zePDgCusU5+fn8/rrrzNp0iSCg4Mr9Rmt7dxovEVmoiAIgiDcvns2YFdSUsK0adPIz8+ne/fuLF68mLS0NEaMGKGcBMiyzNGjR5kwYQKPP/44rVu3Ji8vT6wGJgiCIAhVREZGBkajET8/v7vdlbumOoyBxWIhOTmZefPmMXv2bCVYp9FoCAwMxN7enoiICAYNGsTDDz+sBKwqIkkSDRo0oFmzZhQUFBAZGWmTlXYjsiyzePHiCoN1Vjk5Ofzyyy/Uq1evUm2mpqbywQcf4OXlBcDgwYP56aefiI+PJywsjGeffZYFCxZw+PBhxo8fz7hx4zCbzYSEhCjBPVmW0Wg0jB49Wsm+U6lUxMbG0rJlS6ZPn87u3bv57LPPeOqpp3BycmLbtm288MIL7N69m969e+Pg4GDTL4vFgizLHD9+nDlz5uDh4UFycjIvv/wyGo2GPXv28L///Y8OHTowcOBAlixZwvHjx/Hw8OD555/Hzs6O8ePHExERQXFxsdLfTp068eGHH+Ll5cWlS5fo2LEjeXl57Nu3jz59+vDYY4+JoJ0gCIIg3KZ7NmAXHx/Prl27WLx4MQEBATg6OjJ16lQGDx6Mu7u7st0PP/xAq1ataN++PSqVCldXV1HzQhAEQRCqiKKiogozraqT+3UMrItK5OTk8MMPP7B8+XL27t2LxWIBwNnZmddee43BgwdjNpvRarWVruOn1+sZN24cL7/8Ms2bN+edd96pdL+MRiMxMTHX3cZisXD+/HlMJlO5KbkVKS0t5dChQ3z88cc0atSIWbNmUbt2bYYOHcoPP/zAxo0bGTBgABcuXOCzzz7jzJkz/PLLL0ycONFm2q3RaOTzzz9Xau+99dZbAPz999+MGzeOEydOULNmTQwGA1lZWRw+fJiPP/6YxMREjhw5QuvWrTEajciyjMFgUNotKiri9OnTLFiwgMuXL/Pdd98xceJEmjdvzvjx4wkJCWHZsmUUFBTw4YcfsmPHDr7//nuefvppzp8/z9NPP03Tpk1ZunQppaWllJaWcvToUaZNm4azszPPPfccn3zyCW3btmXChAl06tRJWchMEARBEIRbc88G7DIzM1Gr1Xh4eAAQHh5OYWEhKSkpSsBOlmX+/vtv3NzcOHnyJLm5udSvX59x48bZ3GG0WCyUlpYCZbU+JEm65gqtglAdmEwm7OzsKpyyIwiCIAg3w7qAQ1JSEkajEbVajcFgIC8vjwMHDrBw4UIOHz5sk9Hm6OjIxIkTeemll9Dr9crCGzejefPmQNlCBTdDo9Eoi1hcj5OT0039TgYHB9OwYUPs7e3Zv38/p0+fZseOHZSUlFCzZk00Gg0ajQY7OzsaNmxInTp1ygUDtVotjz/+OKGhoUBZUBOgTp06DBkyhJ9++olmzZqh1WrZu3cv6enpbN68mYKCAjZu3IiHhwcLFiwgLy+PKVOm2LRdr149QkNDcXNzY9GiRTb7Sq1Wc+zYMaKjo7lw4QImk4maNWsiyzLu7u489NBDaDQam/EIDAwkKioKg8FAaGgoERER+Pv7U1paSm5urgjYCYIgCMJtumcDdlfX/1Cr1ciyXO6kLDs7mwYNGjBp0iSysrJ46aWXOH78uE2tjjNnzjBr1iwKCgpIS0tDo9Hg7OyMJEmoVCpkWVbu9lofs04hgLLpCJIkKe99vW2sj12rbbVajdlsvqW2zWaz0pZWq72j/b5yXK1jfafH5N9s+7/clxaLBUmS0Gq1ldqXV/bpTo/3rbQtSRIpKSksWbIEHx8fBEEQ7iYnJ6dqX3e2qo2BNXOuoKCAhIQENm7cyG+//UZCQgImkwmVSoVOp6OwsJCEhASbQJ1arcbHx4dXX32VF198EYPBgCzLtzQGtzrlUqVS0a1bN7Zs2XLNMioGg4FOnTrdVMBOp9Mpv8+BgYH069eP1q1bYzKZ0Ov1xMbGKttabyZf/ZklSSIwMJBatWopfQVwdXWlQYMGeHt7884779CkSRN27NhBw4YNKSwsJDg4mO3bt+Pl5cWkSZOQZblc22q1WjkPuPL8wnpT28fHh7CwMAYNGqQ8n56ejlarrXAcNBqNsg/UarVyHgI3rnEnCIIgCMKN3bMBOwcHB0pKSpSTiLS0NLRaLa6urjbb+fn50bBhQzw9PbG3t6dmzZrExcXZBOwiIyOZM2cOsiwzf/58vLy8ePTRR4GywJfFYlGCHCqVCo1GQ2lpqXKyYT0hsfbFGqwxmUxKIMR6omIymZTXVdS2VqtVpipY24ayjCdr2xqNxiY4p1arUavVGI1GUlNTMRqNBAcH2wQwK9vv67VtdafHxLrNrbYNZSfBlRlvWZZtxlKr1d6xfSnLMqWlpSQkJGAwGPDz8yvX9s3syyvH5E71+0Ztq1QqzGazTS1IQRCEu8nBwaHaX9xXlTGwnnecPn2aP//8ky1btrBjx45KL/oQGhrKoEGD6NevH/Xr17cJAv2XYyBJEv3792fx4sUcOXKkwm3atm1L165db6pNK71ez+DBg/nuu+/466+/MJvNPPbYY0q2HMDx48dZunQpH3/8sc2U2NLSUubOnatMie3fv7/N+/j4+FC/fn2+++47Lly4wOzZs/H19cVisZCUlMSOHTvo169fhf26+nffYDDQoEEDZs6cSbt27ejXrx+fffYZkyZNQqPR0KRJExo2bHjDzysIgiAIwr9Dku/RM8ScnBxGjBhBu3bt6Ny5M4sWLSIzM5Np06axf/9+QkJC8PPz44svvmD37t1MnDiRtLQ03n77bb744gsaNGhQrk1Zlpk3bx6+vr706dOnyp1syLKsTBmpbI0X4c6SZZno6Gjs7e0JCAiokvuguLiYYcOGMX36dKU4tiAIVZP1dy0kJIQuXbpUue8kWZZJTEykuLiY0NDQKtf/O6GqjIHFYmHfvn18/PHHHDp0iLS0NJsba9ei1+sJDAykd+/ePPfcc4SHhys3uKxudwxMJhPDhw9n0qRJeHl5KXXzTCaTEvi6mtls5vjx44wdO5YDBw6QlZWFJEl4enrSvn17JkyYQO3atSvdl9LSUlJSUvD19VVu1qWmppKTk4Ner8fX1xeVSkV6ejp+fn4UFRWRnZ2tPA5lY5yQkEBeXp7Srr+/P2q1mvz8fHx9fZXpxzk5OUDZjWtrmZeMjAwApZwMQFJSkjI1NScnBx8fH2UBkBo1alBYWEhSUhL29vb4+fmRlZVFeno6arUaLy8vDAYDaWlp+Pv7I0kSWVlZSkak9fNKkkRSUhI+Pj5oNBri4+Px9fWtVO2/ivZLcnIynp6eoia1IAiCUO3dsxl2Li4uvPXWW3z++ef8/vvveHp6MmrUKCwWC3PmzGHIkCH4+fkxcOBAUlJSGDt2LBqNhqFDhxIREXG3u/+vsU5luFeYzWabKRC3Ii8vD4PBYHNiJ8sy+fn55R6/VcXFxeTk5ODo6IjBYLitPt/u571X3aOxe+EG7sdjUahezGazkgVcXd3rYyDLMpcvX2bYsGEcP37c5jlrxndoaChdu3YlMDAQo9FIXl4eTk5O1KxZk+bNm1OrVq3rTi/9r8dArVbTqFEjfvnlF/bs2cOZM2dQq9XUq1eP1q1bYzAYbur7VavVUrNmTZv2a9SoQY0aNWy28/f3B8De3h57e3ub51QqFQEBARW2b625J0kSzs7ONtl6VlcG6qyufH/r+6lUKqWvTk5OODk52bRxdTtXfi43N7cKH7/yz9f6DIIgCIIg3Jx7NmAHZcVx58+fr9Szs95BXLJkiXLS5+HhwUcffWQzLfBGJ1hXTj2oatzc3GxOrO6m4uJiYmJiiIyMvK3FC6ZOncrQoUMJCQlRHrNYLMyaNYv+/ftXKgB7o2DT4sWLiY6O5rHHHiM4OFgppnwrwQ5vb+9y2QFViXUa8pVkWaakpIS8vDzl39L1ODo6YrFYKCws/Le6KVSCVqvFycnJpo6QIFQ1V/6+V1f38hjIskxWVpayQqmVwWCgTp06tG7dmp49e9KiRQscHR2V3xfruVtlbzTejTGwBr+6du1Kly5dlMeu/L8gCIIgCMLdck9HHaw1wK529WPW2l03025VdfUKXXeCLMtkZGRgNBqpUaNGpcZHlmX279+Pvb09KpWKVatWceTIETIyMmjatCkZGRkkJCTwzjvv4OLiwtq1a9m1axd+fn6MHDmSs2fPsmzZMuzt7YmJicHZ2ZmvvvqKM2fO0LRpU3r06EFRUVGFd5Ar6svChQu5fPkynp6edOjQgdWrV5Oamkrfvn2xWCzMnTuXLl26EBUVhVar5bfffqNDhw64u7vf8PMajUYSExPJzc3FyckJLy8v5Rg0m82kpKQoRZn9/f1xcnKiqKiIpKQkCgoK0Ol0BAQE2KxcfDWz2cylS5eQZVkpNJ2Tk0NMTIyyjYuLC8HBweWO9aKiIi5evIhOpyMwMBCdTofZbCYuLg4fH58KC1pfPX7W+ojWDMQbOXXqFPb29gQFBYnMvLuosLCQ1NTUSv+7FYR7kZeXV6VuFNzP7vUxWLVqFWvWrFG+7+vUqcOECRNo1qwZAQEBFZ6D3ey5yt0aAxGgEwRBEAThXnVPB+z+LSUlJXe7C7csNTVVqWF3p1gsFhYsWIBer+fNN9+s1GtycnLYuXMno0aNAuDAgQM0btyYkJAQZs6cySeffMJPP/3E6dOnUavVnD17lilTpjB37lx2797NunXrGDZsGNHR0SQkJPDzzz/j6OjI+++/z+eff67U6LMGuSwWi80iEFAWvLSugrp//3569uxJx44d+eCDD+jVqxcuLi4sX76cwYMH8+ijjzJ58mRlZTQXFxf27dtH9+7db/hZY2JiGDlyJFlZWYSFhfHuu+/i6upKQEAA+/fv5+OPPyYwMJDs7GycnJyYOnUqf/75J6tWrcLNzY20tDQCAgIYO3ZsuUVTrC5cuED//v3x8/Njw4YNAOzcuZO33nqLTp06odVqqVOnDoMHD7a5MJJlmcWLF7N9+3YAnnjiCXr06EF8fDzjx49n0qRJyvQbq4qmHZWUlKDX63Fzc7vuRYssy+zdu5fnn38ed3d3vvnmG+rUqSMudO4SOzs7kpKSMJlMVTpzWKjeSkpKMJlMlbpZcL+6l8cgNzeXefPmUVxcDJRNsZw/fz5t2rS5o9/9d2MMrKcUMmA0gwRo1WX/BxA/bYIgCIIg3E3VMmBXXZWWlnL+/HkuX74MQEREBEFBQZhMJk6dOsWjjz7Kli1bMBgMNGrUCCcnJ3Jycjh+/Dg5OTm4uLjQtGlT7Ozs+P333/H398dgMFBaWkpmZibdunXjxIkTtGzZEh8fH3Jzc/Hx8WHnzp20aNFCWQEuNjYWd3d3IiIiOHPmDGFhYRw6dAiVSsXhw4dxcHDAaDSi1WqVeiunT59m0aJFFBUVAWXBusGDB9O8eXMlANuhQwcSExM5ffo0JSUlyLJMcHAwCQkJNrVVJEkiKiqKTz75hE6dOt0w0BEWFsZPP/3E1q1bWblypc1zK1asoGXLlrzxxhukpaUxYMAAEhISaNu2LY888ggGg4GCggIGDhzIgQMH6NSpU7n2CwoK+Prrr2nZsqVSMNqqQYMGjB8/HgcHhwqnC5lMJrZv386HH35IYmIiGzZsoH379nz77bf07du3XO2ca7FYLDfMUpVlmT///JMRI0YQHR2NJEk8//zzfP3119dcRU74d1mnmoksR6Eqy87OpqioyKY2VnVzr46BxWJh3bp1nD59Gij7znnuuedo3br1Hb9R81+PgSxDiQkOJsGvp+FEKqglaFYD+kdBXW/QqkTQThAEQRCEu0cE7KqY26nxkpOTw9atWykqKiIrK4v58+fz1VdfYW9vT3R0tJLFtWvXLh5++GGef/55Jk6ciFqtxtPTk4yMDGrVqoVGo2Hfvn0MGDAAgJSUFJydnXFwcFBWHbNOFQ0KCuLgwYPEx8ezc+dOMjIy6NSpE0eOHCE6Opply5YxYMAAsrOz6d69O4GBgZjNZs6dO0fNmjWVqad169bl008/rfBzxcbG4uvri729PQaDgdDQUF5//XUlk27btm34+fnZvKZGjRpkZ2dz8eJFIiIiKCwsZNSoUbz99ts2tfSgrOahh4eHEjy8cgp2gwYN2LZtGxcuXODChQt4enri5uaGu7u78nprQLGi/Wa9GNLr9TRv3pxff/1VeU6tVhMdHc2oUaPw8fFh8ODB5bLZ1Go1zs7OnDhxQtkPO3bsoKSkhC5dutyx6dMWi4W9e/cycuRITp06BZQF8P7++29GjhzJ3LlzqVev3h29gDObzSxZsoS9e/fyxhtvlKtlaF0F0Jr1Af9XDDs7OxsoC+w6OzsrC5eYzWaysrJwcnKqcPW53NxcDAZDhUFci8WiTIuu7L/BHTt2YDKZaN++faW2vxUiu1EQhH9LTk4OixcvpqCgAIDAwECeeOKJO16a424wmmHJP/D5Psgz/t/j0Rmw6QK8+xD0DAfNTXzFyrKM2WzGbDaj0+mUGyqyLGMymZSFurRarfLdXVpaqjxuLXtivekIZd/x1t+kitq4MmterVaj0WiUWQmA0o+r+2mxWJRVfnU63XX3qbX/JpMJrVaLWq1Wzl/q1atHWFgYpaWlWCwWNBrNdev8XjkWarX6jiwsJgiCIAj3q2oZsLu6pldV4ufnd0vZNLIsY2dnR3h4ODExMUq2XUZGBrm5uZSWlvLmm28SFRVFixYt+Pjjj3nwwQc5ePAgn376KQ0aNFACVTk5OVy6dAlfX1+l/a5duyJJEr6+vnh5eSkBIzs7O7p168ayZcs4dOgQr7/+On5+fkRHR7N69Wo6depE8+bNadCgAStXrmTHjh3KdNbAwMBKfTaVSqUUi/bz86N///4sXrwYDw8PHnvsMfz9/csF7DQaDS4uLsTExBAREaGsmFaZaYWhoaFKsKdFixasXr2aDz/8kPz8fFq2bImLi4uybXFxMTNmzCAsLIwWLVqUays2NpY1a9YwadIkjh07ZvNc3bp1mTFjBl5eXmzfvp3XXnuNRYsWERwcbPPZR4wYwYIFC3BycqJfv37MnTuX4cOHs2nTJnJzc3nkkUdsapxpNJqbnj65e/duhg4dytmzZ8s9t2PHDoYOHcqiRYuIioq6qXahLBBWUVHy2NhY1q5dy2effVZhpmBJSQmvvfYasizj6uqKJEm89tprxMfH895779G0aVNycnJ45JFHeP7555EkiQsXLjBo0CBGjRrFE088Ua7Nd955h6eeeooHHnig3HP5+fmMGDGCiRMn2uyDilgv0Jo3b35zgyEI1ZB15e7q7F4cA1mW2bBhA3/++SdQ9nvTu3dvIiMj/5UbBf/lGMhyWUbdjL1QUFr++bRCmLILGvtCkMvNZdktXLiQ1atX89NPP+Hq6oosy6xdu5bffvsNi8WCk5MTXbt2pUOHDuzevZulS5diNptxdHTkgQceoEePHvTs2RNfX18MBgNeXl6MGDGCmJgYli1bpkwbfuGFFwgKCuKLL77g0qVLqFQq6tWrx6uvvsr+/fv59ttviYuL49tvv7WZZQBlv7tLly5l06ZNWCwWnnzySR599NEK96ssy+Tm5jJr1iy2bt3Kc889x5AhQ1CpVDz00EPY29tTUlLChx9+yMGDB3n66ad59tlnrzk+RUVFzJkzh02bNtGrVy9ef/31yg+uIAiCIFQz1TJgZzQab7zRPSozM5PS0lKbYFllmEwmPvroI/Lz83nggQeQZZni4mIcHR05cOAALVu2VFZ7lWUZBwcHateuzcCBA/nkk0/Q6/UMHjyYTp06YTKZKC0tVbKMatasqZwMXhno6Nu3LwCenp689tprNv0ZNGhQuT6OGTPmpj6TVe3atalduzZQdif6oYce4qGHHlKevzpYZ2VnZ0dOTg5QdqHw7rvvVur9UlJScHJyws3NjTlz5tCuXTuefvppsrKyGDVqFHv27KFjx44UFRUxb948zp8/z9SpU3F0dLRpx2Qy8fXXX+Pv709SUhLR0dFkZGRw4sQJoqKiCAwMVIKWYWFhrFmzhosXLxIUFGRzUl2nTh2mT5+O2Wzm66+/5sEHHyQ6OpozZ85Qq1Yt5syZw0cffaQEGSuqYXctFouFHTt2KAuFXMuBAwd45ZVXmDNnDg0aNLjhxVxqaiqTJ0/G3d0dk8nEkCFDWLZsGZcuXSIyMpLBgwczf/58Dh06xJQpU3jnnXeAsmPNekEnyzIqlYq33nqLyMhIoCy7IC4ujlatWjF9+nR27drFzJkz6d+/P05OTvz1118MHjyYXbt20bNnTyVr8sp9IssyJ06c4Ouvv8bZ2ZnU1FRefvllAHbt2sX//vc/OnTowJNPPsmyZcs4fPgwPj4+PPfcc+h0OqZOnUqtWrUoKCggLCwMtVpN+/btlc978eJFunbtSlZWFvv27eOxxx6jZ8+eIlNOqLbc3d2r/bTue20MZFkmMTGRL774QsnCcnd3V77n/g3/9RisOlNxsM4qoxA2nIdhTSvfZmFhIceOHaNx48bs27ePLl26cPToUb799lveffdd6tSpQ25uLnl5eZw/f57p06fz3nvvUa9ePYqLi0lMTESWZTw8PJg8eTL+/v5IkoTFYuHjjz+me/futG/fnpycHBwcHNi/fz9JSUlMnz4dlUpFXl4earWayMhI3nvvPUaMGFHhQh4ZGRmsWrWKCRMmADBx4kRatGiBt7c3ULb/P//8c7RaLQkJCbz44osMHjwYvV6vZLWbzWZWrFhBq1atqFu3Li+++CIuLi7KrAKTycSCBQuAsoWqQkJCeOGFF3BwcOCJJ55Ap9NV+lxEEARBEKqrahmwq8onCIWFhRQVFd10wC4pKYkjR47w1VdfERoayqJFi/Dz88PBwYFjx44pQRCTycTatWt5+OGHcXBw4KWXXuLZZ59l0aJFLFiwgHbt2pVr++DBgyxfvvxOfLxrslgsSp212wlshIWF8dJLLwFlJ6TWtqxTNDQaTYVTR678LycnB5VKhZubG/Hx8Tz88MM4OzujVquxs7MjNTUVo9HIF198wf79+/n000+pWbOm8n7Wz2KxWAgLC+Po0aMsW7aMCxcuEBsby+bNm22mf0qSRFFREYWFhRVO47Supnz06FGio6OZPn06n332Gc2aNaNevXps3rwZk8mkvNY6FeZ6rBdN+/fvZ9iwYZw5c+aG2+/cuZNXX32VhQsXEhERcd39ZDQa2bVrF5MnT6ZJkyZ8+eWXhISE8Pzzz/Ptt9+yceNG+vfvz4ULF/j00085c+YMP/30E1OnTrW5WCwtLeXLL7/Ew8MDSZJ44403gLIA4kcffcSxY8fw8PBAr9eTk5PD/v37mTp1KjNnzuTo0aO0atUKs9mMLMs203IKCgo4ePAgCxYs4NKlS3zzzTdMmDCB5s2b89FHHxESEsLy5cvJysrio48+4s8//2TRokUMHjyYf/75h759+9KiRQuWLVtGaWkpRqORvXv3MnXqVLp3787QoUOZNm0aLVu2ZOrUqbRr165SKyILwv0oPT2dkpKSSmdV34/utTEwm80sXLiQgwcPAmXZdc8//zx169b9197zvxwDoxlOp19/G7MMZ9LBZClbiOJGrL+DQUFB9OnTh6+++op27dqxfft26tWrR4sWLZAkCScnJwCWL19OQEAAzZs3R6fT4ezsjLe3N/n5+aSkpPDDDz/g4uKCl5cXPXv2JCAggA0bNpCXl0ezZs2oUaMGNWrUIC0tjcWLF9OwYUOaNWuGJEl4eHjg5uZWbpEqq/Pnz+Pt7a2U2XBwcCA2NtYmYLdz504aNmzIiBEj8PLyQqvV2swgkGWZ06dPEx4ejlqtplatWja/YxaLhePHj6NSqRg1ahTz5s1j5cqVDBkyhODgYFxdXcnMzKzM7hIEQRCEaqtaBuyqYyaLu7s7rq6uzJo1C29vb3bv3k1kZKSygmtpaSmTJ08mKysLtVrN448/zuTJkzGZTLi6unLixAn69++PVqtFp9NhZ2enZCpGRUUpgZLbdWUQzcpoNDJz5kwyMjJ48cUXqVWr1i23f2Wwp7CwEC8vL6BsumO3bt34+uuvy12Q5OTkMG/ePI4fP87JkyeZN28eTz31FAEBATzxxBN8//33nD9/nvT0dAoKCnjooYdYtWoVs2fPpl+/fqxatQqATp06ERUVxbZt25g/fz7z589nyJAhPP300wCsWbOGZcuWMWLECDQaDV9//TWxsbE4OTnxzz//0Lp1a+rWrVvh8VtQUMCPP/7I008/jYODA40bN2bZsmV4e3sTEBBwSzViCgsL+fHHH6+bWXe1gwcPsmLFCt56660bZmEEBwfTpEkTHB0d2bt3LydOnGD79u1KQFqr1aLRaLC3t6dhw4ZERESU+xxqtZquXbsSEhKCJElKFmOtWrXo27cvJSUltGnTBp1Ox7Zt28jOzmbPnj2YzWb++OMP3NzcmD9/Prm5uUydOtWm7QYNGlC7dm08PT1ZvHgxRqNRCcpqNBqOHDnC6dOniYmJwWg04u/vj8ViwcPDg/bt2yu1iK78vA0aNFDqLEZFReHn50dpaSk5OTkiYCdUW0aj0aYWZXV0L42BxWJh8+bNzJ49W8muq1u3Ls8///x1a5Pdrv9yDFQS6CoRhNOpy7atjJKSEn777TcaN25Mamoq8fHxxMTEUFJSgsFgUH67rXXtiouLyz1u5eDgQFRUFF5eXjg6OmIwGBg+fDiHDx/m0KFDfPDBBwwYMIBevXoxZcoU/v77b1avXs2SJUuYOXNmuYx+gE2bNvHzzz8TGhpKixYtUKvVyqwKtVqN2Wy22d7V1ZWOHTsqK83fSvajJEn07t2bkJAQunbtyoYNG5QafIIgCIIg3Fi1DNj9myec/zZHR8cKs6xuxMHBgU8++YSTJ0/i4eFB3759UalU2NnZMXLkSHx9fTl37hwajYZmzZrh5ORE//79iYuLw2w2061bN6XWm729PWFhYcTHx1O7dm3s7e3LTS28Gfn5+fz0009kZ2cTFhZGnTp1WLVqFaWlpbz00kv88ccf/Pnnn7z44os8+OCDdyTgWlJSQm5urhL8MxgMvP/++8qJ6ZW0Wi1169aldu3aPPbYYxQWFioZjo8//jh169YlLi4OvV5P3bp1qVGjBg0aNGDmzJk2J7gODg5A2YXPa6+9hr29vU0Aqk2bNgQGBiqPde7cmejoaCXo1LhxY+XOfEUGDRpEkyZNkCSJzp074+bmRmFhIY0bN7Z5H5VKdcOTZesd9//9739ER0ezbdu2G2blWQO9L774YqWmTOn1eqUv/v7+DBw4kBYtWmA2m9Hr9cpqxtb+VNRnlUpF7dq1lSmx1mPDw8OD5s2b4+fnxwcffECzZs3YsWMHYWFhxMbG4urqyv79+/H29mbSpEnIslzuGLZmW165CqtKpcJkMiFJEp6envTu3ZuBAwcq03PT0tLQarUV9vXK7E1rMM96sXQvTYUThLuhOt5Iu9q9MAayLHPq1CnGjRunrFqu1Wp59dVXCQ8P/9f7+F+NgUYFrfxhb/y1t9GqoIVf5QN2KSkpXLx4EScnJy5fvoxWq2XPnj1ERUWxbNkycnJycHJyUhZ7CAsLY+nSpeTl5Sm17qwBSycnJ1q3bm1Te06tVvPggw/y0EMPsXHjRjZs2MCjjz5KSEgIYWFhdO3alRdffJGUlBQcHByU32zrb0y7du1o1aoVarWalJQUMjIyyM7ORqPRkJOTg6enp+0YXVHv9spZBta/X+nqmQhXvnd2drayaJOdnZ0y08DaxrXq2AqCIAiCIAJ2VY6jo+Mt3+UMCQkptwIq/F/dudDQUJvH69WrR7169cptr1areeSRRzhz5gyPPPLITfflanl5efz222+8//77BAYGMnHiRF566SXOnj3Lxo0bqVevHoMGDeLFF1+87feyio2NJTAwkICAAKDsgsS6cMXVHBwc6NmzJ1B28mk0GpWAjE6no2HDhjRs2NDmNXXq1KFOnToVtmedxnI1f39/m4BhaGhouX1yLY6OjjaLWuj1eh588MEKt5UkqdKrnIaEhDB//nxee+01NmzYcM3tVCoVgwcPZtq0aUrWYmXpdDqeeuopli5dyt69ezEajfTs2dMmOHns2DGWLVvG5MmTy02J/eabb5T37N27t03bfn5+REZGKlmQM2fOpEaNGlgsFt577z127dpV7jXXYjAYiIqK4quvvuLhhx+mb9++zJw5k4SEBKXYd4MGDW7qswuCUJYBfnV2T3VzL4yBLMvEx8czcuRIjh8/DpSdLz3//PM89dRT/3pA5b8egz6RZXXsLuVU/HxdL+gUVvn2tm3bxoMPPsioUaNQqVScPn2aTz75hFmzZvH3338zduxY6tevT0FBAX5+fvTq1YsOHTowZswYGjZsiMlkQq1W8+yzz5Kens4vv/yCm5sb9vb2SuZ+UVERTk5OHDx4kE6dOnHixAnWr1+Pj48PaWlpuLu74+Pjw6VLl1i9ejUXLlzgu+++o2/fvjRo0EC54avVagkODuaTTz6huLiYpk2bXncxpZKSEtatW8e2bduUKbdXnjOZTCZ++eUX/vrrL9RqNa6urvTs2ROVSsXKlStJSUlh3759DB8+HLVaza+//sqWLVsoLCzEzc2Np59+WqwWKwiCIAgVkORqlNohyzLz5s2jRo0a9O7du8rdzZNlmaSkJIqLi5UpgHdLYWEhX375pVJk+HacPXuWRYsWMX78eP7++2/GjRtHgwYNKCws5OGHH1bqpliDZrdLlmXWr1+Ps7MzDz300E2NoyzLXLx4EYPBYLPyalVSXFzMq6++qgTXrHX5TCaTUgvuaufPn+e1115j8+bN5TLttFot/fv357PPPsPHx6dSfTAajcTHxxMYGIhGo8FsNhMfH09GRgYGg4GgoCBUKhVJSUmEhIRQUFBAWlqa8jiU3ZW/cOECWVlZSruhoaFKtkBgYKByd9+aKRISEqIEK1NSUgBs+nz58mXc3d2RJImMjAwCAgIwm83ExsYSFBREfn4+ly5dwsnJiZCQENLS0khMTESj0Sg1IePj4wkLC0OSJNLS0rBYLEq9w8DAQCRJ4tKlS8pU5fPnzxMYGHhLmbNms5nk5GQ8PT1v6fVC1Wf9XQsJCaFLly5V7jvJmlVksViwt7evcv2/E+6VMUhOTuaNN97g119/VQJnbdq0YdmyZcriB/+W2x0Dk8nE8OHDmTRpUqV/10wW2BsHE3fCpWwo+f+xQjtNWbDu/bbQwKfyGXZnzpzByclJufFmNBo5fvw49erVw2w2c+rUKTIzM3FxcaFOnTo4OztjNBo5efIk6enpODg4EBkZiYuLCzt27KCoqAgou1nUuHFj8vPzuXjxIiUlJfj4+BAZGUlpaSnR0dGkp6djZ2dHREQE3t7epKWlceDAAaDshlpUVBRBQUE2452VlcWJEyeQJKlcBr8sy/zzzz8EBATg6uqK0Wjk8OHDym+pu7s7jRo14uLFi/j4+ODi4sLevXvJzc0FwM3NjcaNGzNmzBilzq+Pjw9RUVFoNBr27t2r1K+zs7Ojbdu2ys108bsmCIIgCP+n6qaa3YaqHKO0rtB6t9nZ2dGxY0dOnjxJq1atbqseyeXLlwkJCVGmEnbu3JlRo0aRnZ2Nl5cXkydP5qmnnrpjfS8oKKCkpESZPnqzjEZjlc7ShJv/NxAWFsa8efN46aWX2Lx5s/K4JEkMGDCAGTNmKMWqK0On09lkD6rVaoKCgmwuKOD/sj4dHR3L1eRRqVTXrGdoDSJLkoSbmxtubm7ltqkouHjl+1vfT6VSERZWlmbh6upKo0aNbNq4up0r+3RltuGVn9faHqCscCwI1VVaWhrFxcXV+t/C3RwD683AMWPG8Msvvyi/D1FRUcyePftfD9ZZ/ddjoJagTSAs7QdbL8LJtLLHGvnCI8HgqIOb+dTW0gxWOp2OZs2aKX9v3rx5udfo9XqaNGlS7vH27duXe8zd3b3cghx6vZ6mTcsvY+vt7c2jjz56zb5KkoS7uzsPP/zwNZ+vX7++zWdp1apVue2ioqKUPz/00EM2z1nrHPv6+tKmTRub51q3bn3NvgmCIAiC8H+qdtShGrpWLa+70Y969eoRHx9/RwKg1uBZ48aNOXz4MO+88w7h4eEMGjQIX19fmzoutys/P59OnTpVWJS5MqyFmqsTSZIICgriyy+/5PXXX2fjxo2o1WoGDhzIJ598UunMOkEQhIpU5Rtpd8rtjIEsy+Tn5yv1OCuzorosy+Tm5rJ3714mTJjAwYMHlT4EBAQwZ84cGjZs+J9m/P2Xx4EklQXkPOxgQBRY/v9bWzPqqmGy5x2l1WoZO3bsbc/CEARBEITqrFoG7Kpyir2Xl9ddr3NjpdForlvzpLI6deqk/Nne3p4RI0bYPD9s2LDbfo8rWReMuFV+fn6VrgF3L1Kr1beUIShJErVq1WL27Nm88soreHl58emnn4pgnSAIt+VWv5PuJ7czBhaLhb/++osvv/yS0tJS/P396dOnD4888ggajUZZBMBazkClUlFaWsrevXv58ssv2bRpE3l5eUp7/v7+zJo1i3bt2v2nN6f+jeOgMgFAa2BOLQJ0d5QkSfj5+d3Sa0UAXxAEQRDKVMsz5ButdnkvKy0tvWcCdtWVdUpsVQ383u6qpKGhoSxduhSdToezs/Md7JkgCNXR7d5EuR/c6hjIssyhQ4cYPnw4Z86cUR7/5ZdfeO6552jfvj2XL1/m4MGDXL58GbVaja+vL7GxsRw8eNAmUKdSqYiMjGTatGl069btP88kv9PHgVarJS8vj9LS0ip9k626KS4uxmw2i30mCIIgCFTTgN29UAPuVmVlZVFUVCSmGNxFKSkp2Nvb3/KU2rvNYrHcUtBXlqHUAqkFEulmT1TF4KWS8HIoy0wQ04cEQbgVWVlZGI3GClfPri5udQxycnIYP368TbAOICMjg08//ZQvv/ySoqKi696kkSQJV1dX+vTpw1tvvUV4ePhdCZbcyeNAkiQMBgMGg4Hk5ORqV8aiqrLeUHR1dRUBO0EQBEGgmgbsBEG4ObIMGUXw7RHYcB4uZUtIEoS7Q786MLAeOOtuLmhnPTGXZRmVSoUkScpFpcViQZblcjUbK3r86uCj9SS/om2tU8MA5T2t21792JX9vNE2V29vfW/rthaLhd9//506deoQEhJi8/z1LiSt42OxWG64rSBUVYWFhRQVFVXrgN2tjIHZbObbb79ly5YtymN2dnaYzWal4H9hYeF123B2dqZnz54MGzaM5s2bo9Vq79oqtXf6OFCpVHh6emI2m6v0zIrqpjrWCRYEQRCEa6mWAbuqfCJQVadh3k/s7e2r9H6QJOmmL8iyi+GDP+H3GDD//0QNWYYzGfDpXojPhXcfAsNNfqN89913rF27lh9++AFnZ2dkWWbz5s2sXr2a4uJiXF1d6dGjB23btuXgwYP8+OOPFBQU4OLiQrt27ejcuTN9+/bFzc0NvV6Pt7c3I0eOJCEhgaVLl1JQUICDgwNDhw4lODiY+fPnc+bMGSRJolGjRrz88sscPHiQb7/9luTkZObNm4e/v79NHy0WCytXrmT9+vVYLBaeeeYZOnToUOEYyrJMXl4ec+fOZdu2bTz77LMMGjQIlUpFkyZNcHZ2pqSkhEmTJnHgwAGefvppBg8efM3xKSoq4quvvmLTpk306tWL4cOH39wAC4Jw30pOTmbx4sWUlJQAZatPz5kzh+TkZGbNmkVsbCx5eXnY29tTo0YNfHx8MJlM5OXl4eDgQL169Xjqqado3rw5Dg4Ody1Q92+xfp7qXh9REARBEISqq1qexeh0urvdhVvm4eFxt7tQ7d1qEeV7xc0W9pZl2B1nG6y7ktEMK05DrwhoVqPyWXbFxcUcOnSIsLAwDhw4QPv27Tl58iRz585l9OjRNGzYkKysLHJycrhw4QITJ07kf//7H02aNKGwsJC4uDgsFgtOTk5MmTIFPz8/5QJt+vTptG/fno4dO5KZmYmTkxP79+8nJiaGqVOnolKpyMnJQa1WExYWxujRo/nf//5X4VThrKwsfvrpJ95//31kWWb69Ok0atQIT0/P/z8+Ml9//TUajYbLly/z7LPP0q9fPyRJIj8/HygL+m3evJlmzZoRERHBk08+icFgoKCgAACTycSPP/4IwIkTJ6hduzaDBw9Gr9fTs2dPZFmmuLi40vtMEKoSJycnDAbD3e7GXXWzY2C9uXHs2DGg7Hv9+eefp3PnzqhUKjp37szx48eJj4/Hy8uLFi1a4OvriyzLFBQUYGdnh0ajuaeCdOI4EARBEARBsFUtA3bWqSJVUUZGBiUlJQQEBNztrlRbiYmJSjZXVWQ2mzGZTJXe3iLDmuiKg3VWhaWw4Rw0rQGVvfzbvXu3sprhN998w4MPPsi2bduoW7cubdu2RZIkpVbjihUr8PPzo23btuh0Otzc3PD39yc/P5+MjAxWr16Nq6srHh4etG/fHl9fX3bu3IlaraZ+/fq4ubnh6elJZmYma9eupW7dujRo0ACVSoW3tzeenp7XDGKeO3cOLy8v6tevj0qlws7OjsuXL9sE7P744w+ioqJ44YUX8PPzQ6/X4+bm9n9jaLFw5MgRAgMDiYqKom7duvz55582z+/fvx+z2cwbb7zB/PnzWb16NU899RTh4eF4eHiQmZlZyZEVhKrFwcGh2k9ZvNkxyMjI4JtvvlGm7Pv7+/P0008rJQH8/f3LZQtb3auLBYnjQBAEQRAEwVbVnRt6G6ryCWFeXh65ubliyfu7qLCwUJmCVBXd7CqxpRZIv34ZJGTKtjFX8p9WSUkJGzZsICgoCKPRSFJSEhcuXKCwsNBmapb1/9aprVc+bv2zVqvF3d0dLy8v3Nzc0Ol0DB8+nI4dO3LixAnGjRvHhg0bqFevHu+99x4mk4kffviBMWPGXLO+06ZNmxg6dChTpkwhNzcXjUajXAhrNJpyAU8XFxe6dOlCaGjoLWeIyLJMv379iIyMpEePHhw7dkysCC1UC+np6SQmJt7tbtxVNzMGFouF3377jSNHjgBl34dPPvlkla8BKI4DQRAEQRAEW9Uyw64qkmWZ1NRUVqxYAYCjoyNBQUH31HSW6qK6jblWBa43iEFJgJsdqCt5CyA1NZXTp09jNBo5dOgQRUVF7Nu3j4iICFauXEl+fj4ODg7IsozJZCI0NJSff/6ZoqIiJRPOminr6upKp06dbLJJdDod3bt3p3v37mzcuJHffvuNzp07U69ePerXr09GRgZDhw4lKSmJ0NBQJYBpDWa2bduWZs2aodFoSE5OJjMzk7y8PDQaDbm5ubi7u9uOkVarBOqubOvKhTSsrn6vK5+3TpEtLCxEp9OVe731z9XtGBTubyaTqUqv3n4n3MwYZGZmMnv2bOWGQ3BwME8++WSVX1VTHAeCIAiCIAi2qmXATqvV3u0u3LT4+HjeeOMNDh06hF6vZ8uWLcyePZvIyEhx8f4f8/b2rtJFrFUq1U1d2Kkk6BkOf10umx5bETsNdAmr/HTY7du388ADD/DWW2+hUqn4559/mD17NjNmzGDXrl28//77NGnShNzcXFxdXXn00Udp1aoV48aNo0WLFhiNRkpKShgyZAhZWVls2LABd3d37OzsaNq0KatXr8ZiseDq6sqOHTt44IEHOH78OFu2bKFmzZokJyfj5OSEl5cX8fHxbNq0idjYWFasWEH37t2JjIxUAnA6nQ5fX19mz55NcXExUVFRhISEXPOzGY1Gtm7dyt69ewGoWbMmbdu2VZ43mUxs2LCBffv2oVar8ff3p0OHDqhUKpYvX05ubi7btm3j+eefR61Ws3HjRnbu3ElBQQHLly+nb9++VfI7TBCu5Wa/k+5HlR0Ds9nMsmXL+Oeff4Cy4P3AgQOpW7dulT8XEMeBIAiCIAiCraobdbgNVWmVWFmWSUpKYsyYMaxatQonJyc0Gg3nzp1j5MiRzJ8/n+Dg4Cp/ol6VGAyGKnUMXe1mV4mVJHg4CNoGwo7Y8kE7tQTdakET38ovOFG3bl3atGmjBMXq1q3Lc889h52dHePHj+fgwYOkpqYSHh5O06ZNcXFx4a233uLAgQMkJyfj7e1NkyZNMBgMDB8+nIKCAoqLi5EkCZVKRYcOHTh9+jTFxcUMGTKExo0bYzQaKSgoIDk5mcjISAYOHIiTkxN5eXkYDAZGjRqFSqUqt2/1ej3jxo1j3759qFQqHnroIZuArSRJvPDCC4SGhiqPabVaOnfuDJQF/NRqNYMGDSIgIABJktBqtXTr1k3ZFsqKxvfq1QutVsuwYcNo1qyZsm379u2VtgThfuPp6Vntp39XZgxkWeby5ct88803SiZacHAwzz//fJW+iWQljgNBEARBEARbVf8M7xZUpfpjycnJDB8+nHXr1iHLMo0bN8bd3Z3Vq1ezdetWnnvuOebPn094ePjd7mq1ERcXh52dHTVr1rzbXbklN7voBIC7HUztAF8ehG0XISGvLPMuyAV61IbnGoHhJr5NGjdubPN3vV7Pww8/rPz9kUceKfcaOzs7m0w1q759+5Z7zNvbm9q1a9s8ZjAYbN7Dyt/fn0GDBl2zr5Ik4e3tTa9eva75fKtWrWw+S6dOncpt16xZM+XP3bt3t3nOOr03MDCQNm3a2DxXUVuCcD8xGo2YTCbs7e3vdlfumsqMgSzLfP/995w4cQIoC+C/9NJLBAcH/0e9/HeJ40AQBEEQBMHWHQ/YybJMaWkppaWlSJKEXq9HpVKJDLCbJMsy8fHxvP3226xbt06563zllBFZltmxYwcjRoxgzpw51K5dW4zzf8BisVS7RT8kCWo4wnsPwTMNIDm/LGDn5wQBzqBRVT67TihPo9Hw+uuv4+Xldbe7Igj/uezsbIqKimxWVq5ubjQGsiwTHR3NDz/8oCycFR4ezjPPPHNfZNeBOA4EQbjziouLWbZsGZs2baKwsJAhQ4ZUeKP3Sjt27GDGjBk2jxkMBsaMGaMsYBYdHQ2UzY6oWbMmjz/+OG3atLG5Rtu4cSNff/01KpWKt99+m1atWl3zOu306dOMHTuWsLAwpk+ffsPv9by8PGJjY3FycqJmzZq3PPPn6NGjTJ48mWbNmvHmm2+KmRz/ktLSUj755BP+/vtvPDw8mDx5ss1CUaWlpRw5coQ1a9Zw/vx5dDodzZo1Y9CgQXh4eGA2m/nnn39YvXo1Z86cQa1W06hRIwYNGoTBYOCDDz4gLi7O5j0feOABRo8ejcViYdeuXSxfvpzU1FRcXV2JiIjgqaeews/Pj5SUFJYsWcLBgweRJEkp1dOxY0fUajXbtm1j/fr1XLx4EWdnZyZMmEBQUNA1P6vRaGTLli2sWrWKgoIC2rZty4ABA8rV/76eo0eP8uuvv3L69GlUKhVvvvkmDzzwAADnz59n/Pjx+Pr6Mn36dAoLC1myZAl79+4lMzOTxo0bM3bs2EovAhgdHa2UGjGbzbz88st06dKl0n39L9yRszxrYfjTp0+zfft2zp49S1ZWFlqtFl9fXxo3bszDDz+Mt7c3IAqmV4Ysy3z00Uf8/PPPNsGh0tJSmwxBWZbZtGkTb775JitWrLjlFSqFyrtyxdD7yY2CkJIEejXUdi/77+rnhFunUqkICwu7pddWt+BxVVdSUsKWLVuIjY0lMjKSNm3a2Jwgy7LM/v372bNnj/JYs2bNaNOmTZWeii/cnqKiImbPnk1sbCxQNpX+9ddfr/IrwwqCIPybjh8/zogRI9Dr9TRt2lS54XE9CQkJrF27Fm9vb5o2bQqUnfvLsqwEP/bu3UuLFi2QJIkNGzawcuVK1qxZo2xvMplYsGABa9euBcpmc7Rs2fKa18CZmZmsXbuWJk2aVOq8bu/evfTp04fevXuzaNGiW77+sybaiHIE/66UlBS++uor4uPjUavV9OjRg379+gFlx8rChQt5//33MRqNREVFoVKp2Lx5M7Vq1aJr1678/PPPjB49moKCAiIjI9Hr9fz11194eXnRuXNn/vzzT86dO0eLFi1wdnYGUPbpnj176N+/PxqNhqioKGJjY9m0aRMNGzbE09OTN998k1WrVhEVFYWrqysHDhwgPj6ehx9+GEmSWLRoEdu3byctLQ1XV1dGjx59zc8pyzLLly9n+PDhuLu74+rqysqVKzlx4gSffvopdnZ2lRqv9evX891335Gbm0tpaSlPPPGEzXtcecxmZmby8ccfU1xcTHJyMiUlJTd1PP/5558sWLCAvLw8ioqK7rlgHdyBgJ3RaGTv3r18/fXXXLp0SYn0+/n5YTQauXDhAnv37mXWrFl06dKFQYMG3fKF6Z1SFYJakiTRunVrVq9eTWZmpvL4gQMHUKlUNl/mTk5OPPjgg/fNXfZ7XVVfnVetVpdbtECj0ZCfn4/JZBJBgSrE+qN0PwaQ7yWyLFNSUoLRaESlUmEwGFCr1Tf9PWAymfjqq684duwYbdq0Ye7cuSQlJfHkk0/atLV582ZOnjxJ3759UalUuLi43OmPdE8Rd/RvPAb79u1j6dKlysVm27Zt6dGjx3/Rtf+MOA4EQbgW66yiVatW0a5dO3Jycrh06RKvv/46AL///jvHjx/HYDDQpUsXmjZtytmzZ5kyZQqFhYX4+flRq1YtQkJCOHjwID/++CPt27enZ8+e1/wtb9GiBStXrlT+rlarlRImAJMmTaJVq1b06dOHv/76i+PHjysBuwsXLvD333/j6+uLyWRiy5YtpKWl4ePjU6nPe+rUKb777jvCw8Px9/dn165dhIaGMmDAAJKSkpg/fz5Go5FDhw7x1ltv0bhxY5577jnS0tL47bffiI6Oxs3Nje7du1O3bl1UKpWSRdWvXz+OHDkCQMeOHQkJCVGSav766y9Wr15N27ZtycrK4tKlS3Tv3p2QkBCWL19OWloavXv3pnHjxuJ6oZJkWVb2f1hYGAkJCaxcuZLevXujVqs5ffo0H374IUajkQULFtCzZ09UKhXR0dG4urpy6dIl3n33XXJzc5k1axZPPfUUGo2Gixcv2hy7jo6OfP755zRq1AhAqeu9adMmMjMzmTt3Ls899xySJHH58mVcXV2Ji4tj9+7dRERE8Ntvv+Hp6Ul+fj6JiYnodDpUKhUfffQRFouFxx57jLS0tOt+1uzsbL744gsl0BcZGUn37t354YcfeOGFF2jSpInN9mfOnGHhwoVERETg5eXFvn37GDp0KM888wyDBg3ijTfe4I8//rB5jV6vJygoSJmV5O3tzfr16zlz5gyDBw8u16eFCxdy9uxZunXrxokTJ0hJSaFLly488MADqNVqevfuTffu3XnvvfdYtmzZrezif91tR3hSU1OZMmUKvXr1YsyYMYSHh9tET2VZJj09nePHj7N+/XrmzJnDzJkzb/dtb4u1WPO9TJIkBg8ejMViYfTo0eTl5QFlwSIHBweOHj2KLMvo9XrGjBnDyJEjRcDuP5KRkYFOp6uy03ZkWba58yBJEnZ2dhQUFJCUlCR+gKsQi8WCs7OzCNj9C2RZxmg0cvz4cbZu3cq5c+fIzs5Gq9Uqi5506NABPz+/Spd9sN61nz9/PmFhYYSEhDBnzhx69uyJk5OTzbYRERH07NkTjUaDRqOp0jcJbqSyFzD3s2uNgSzL5ObmMmPGDHJzcwGwt7fnzTffxMfH5746LsRxIAjC9Rw5coRZs2axcuVK1Go13t7eDBgwgI8++ogNGzbg4+NDfn4+X331FXPnzsXHx4d//vkHi8VCeno6+/bto2/fviQlJTFr1ix0Oh09e/a85vslJiayYsUKoCzZo1OnTjbnW2azmdLSUiwWC3Z2dkoAQZZltm3bRlpaGsOGDSMpKYnffvuNvXv30rt370p9b1+6dIm5c+fi4OCAu7s7GRkZ5Ofnk5+fT6tWrThx4gQWi4W0tDR27tyJTqcjOTmZIUOGsHfvXmrUqEFmZiZff/01ixYtom3btvz+++8sWbKEdevWIUkSUVFR1K5dm3nz5vHoo48yaNAgZYxXrFiBXq8nMTGRRYsWUadOHS5evEh8fDxr1qxh/fr1VbaW93+tuLiYDRs2YLFYGDt2LB9//DF79+7l8uXLhISEsHXrVtLT03n00Ud59NFHlThK/fr1AViyZAlxcXE88MADPPHEE0qdV2v9+qSkJKAsiWrr1q3ExMQor4+KisLe3h5ZlpUMv8aNG9OmTRu8vLwoKSlBo9Fw/vx5Ro8eTevWrWnZsiUNGzZUzm1r1apFRkZGpY7bpKQkYmJicHFxoWHDhtjb29OgQQOOHDnCkSNHygXsLl++zJw5c3B2dsbOzg6dTkevXr1o3bq1cpP8amlpaSxcuJBatWoxatQo9Ho9derUUWYgXO23337jt99+49dff0Wj0RAXF8fChQtZunQpjzzyiDJT4V6+lrrtCI+vry/Lli3DxcWlwkGVJAkvLy/at29P27Ztyc/Pv923vG1VJe3XYDDw7LPPIssy77zzDpmZmfj7++Ph4cHRo0dxcHDg3Xff5fXXXxdFmv9DOTk52NnZVdmAncViKTclQKVS4e3tjclkqtR0AeHeoFar7+kfmKqqpKSEbdu2MX/+fFJSUvDx8SEwMJCgoCCMRiNJSUn89NNPfPXVV7Rr145nn32WOnXq3LDd9PR0AGrUqIEkSdSrV4+8vDwSExOJiIhQtnN2dua3335j8ODBykrIrVq1svmNtVgsmM1mJQB/5WIyarVamboDZb/DarXaZrEZ63Fz5e+hRqNR2gSUVZOvft2dbjslJQWj0UhwcLDN95O17Stfd6v91mg0/1rb1xqTq18nSdI1205JScFkMhEYGGjTtkqlYsOGDezcuVNp+7HHHqNt27Y2+/te2Zc3MyZXt20dg5o1a95027Isi/IAglBN+Pn58eOPP+Lm5sbWrVtZu3YtdevWZdiwYcTGxjJjxgw+//xzNmzYwMSJE3nmmWfo3Lkzs2bNwtXVlaNHj/Lqq6/SunXr677P8ePHeemllwDw8vJSpiBajRs3DlmWOX/+PEOGDFEWNjMajaxZswZJkujVqxfJycmsWLGCdevW0bNnT3Jycjhw4IDy3Xf1YmNXcnZ25qeffuLcuXM888wzbN68mWHDhjF16lSefPJJHnnkEb788kscHR1ZsmQJW7dupUOHDgwePJjDhw/zxRdf8OWXX9K8eXOlzQceeIDp06ej1+s5dOhQhe9bt25d5s+fz6hRo/j1119p27YtCxcu5IUXXmDbtm1cuHBBBOwqKTExke3btxMUFES3bt3YvXs3ixcvZu/evYSEhJCYmIgsywQEBKDX65XXWQNk1ucDAwNtMtGvDqAVFBQwceJEZQbIO++8Q1RUFP379+f3339n3759nDp1Cp1OR3BwMHPnzqVdu3a8/PLLTJ8+nZ9//pnly5fj5OTEwIED+fTTT68bX7BYLJw4cYKEhASgbKV3jUZDQUEBjo6O2NnZIUkSDg4OQFnCy7VotVqWLFlCZGRkuRvYd4LFYqFPnz689dZbfPnll0yZMoWFCxfStm3bKnEdddsBO41Gg6OjI0VFRco0oStZa67Z2dmh1WqrbJDjbtHpdAwZMgSAMWPGKI87ODjw9ttvi2DdXXA/XhRYv/SvniorCNVRWloaX331FY899hgtW7YkODjYppSCLMtkZWURHR3Nxo0bWbBgAZ999tkN2zWZTDZTaQ0Gg1KL40r9+/enT58+aLVafv/9dz744AOWLVuGp6ensk10dDRffPEFBQUFREdH88QTT+Dl5YVWqyUsLIz8/Hzi4+OBsrIJ4eHhnDlzhqKiIgBCQ0NRq9WcP39eydaOjIwkLi5OKcPg6+uLh4cH586dw2g0otFoqF27Nvn5+UpxY2dnZyIiIjh9+jTFxcU2bV+4cAGz2YydnR1hYWEkJycrQUsfHx98fX35559/lAxyHx8fcnJylH47OzsTGhrKuXPnKCgosGk7JiZGyWqoVasWiYmJysmgj48PNWrU4Pjx40pQp169emRnZ5OQkIAsy7i4uBAYGMjFixfJz89HkiSCgoIwGAycOXMGKPv9rVu3LrGxsUrbvr6+eHl5cebMGUpLS1Gr1URERJCbm6v028XFhaCgIC5cuGDTtl6vVwqVazQaGjZsyKVLl8jMzCQ7Oxt7e3scHR2Jj49XxtvHx0eprQJldZBeeuklLl++TGFhIQAhISFotVrOnTtnsy/j4+Nt+u3p6cnZs2eVtmvVqkVhYaFyV9rJyYnIyEib4yQkJASNRnPdfent7U2NGjU4efKkUlYhMjKSvLw8m+MkLCyMs2fP2uxLjUZDTEwMZrOZwsJCfH19uXz5stJvb29v/P39OXbsmLIv69atq4y3dV/6+/vbTFUTBOH+1aFDB8LCwpAkiZiYGCUb/o033kCWZYqKikhOTiY3NxcHBwdlEUQXFxd0Oh0tWrSgRYsWN3yftm3b8vXXXwNlNwp8fX1tbl64ubmRn59PcXExubm5ys2D6OhoDh06hLu7OxcvXqSwsBBHR0e2bdtGcnIyly9f5tlnn1V+H3bs2HHNPlizpEwmEwaDgdzcXDQajfK5tFotrq6u6PV6Tp06hcViYceOHezdu1e5sXfhwgXlOx2gX79++Pr6IknSNbOmWrduTUBAADVq1ECj0dC6dWv8/PwICAjAYrEo3+PC9cmyzB9//EFmZia1atViz549ODk5YTabWblyJQMHDsTd3R1JkkhLS6O0tFSZMWe93vTw8ADKZjVenXR05TWpi4sLP/zwA1FRUQBKu7Vr12blypXs37+f7du389tvv3HmzBnmz59PmzZteOONN+jevTs7duxg69atSibm008/fd2gtslkYu7cuco00o4dOzJx4kQMBgMWi4WSkhIMBoNybni9QFyTJk1o3LixEty70/R6PW3atMHX15c2bdpgZ2dHXFwcxcXF/9p73km3HbCzHnDnz5/njTfeKPeh09PTmTx5Mi+++CINGjS4J6ZvVIVI6pUMBoMy53zhwoWUlJTw7rvvMnLkyEoXbxTuHFdX1ypda8da00AQhIrVqFGDpUuXKifEV5MkCXd3d1q1akWLFi2UwMmN2NnZYTQabQrlajQam5MYSZJsFhLo3Lkzc+bMIT8/3yZgV7t2baZMmYIsy3z77bcEBQVRt25d5cLEYDAote/UajUqlYpatWopQQ/rXVzriZ31xN96gg5lAXy1Wk14eDiyLCtt6/V6paix9fe0Vq1ayomjte3IyEigLEtKr9fj7++vTHvUarVoNBoiIyNJTEykpKQEvV6Pp6enTb81Gg2hoaHl+m3NaLyybV9fX5u2r8x6NBgM5drW6XSEhIQobVvrtdStW1cZE7VaXWHbERERyuc1GAzodLobti1Jkk3bKpUKHx8fzp49yw8//MDRo0dp3bo1jz/+uFKXdu3atTYLkPTs2ZNWrVop07CubPvqfVmzZk2bfle0L+3s7JTjz/q7cOVxYm37evtSo9Gg1WqJjIy0GZMrjxPrCvcV7Utr24mJiVgsFpvxtk4Jv3pf6nQ6m2NQpVKJG06CUE1Y/+1DWVaPWq2mQ4cOfPTRR+j1eoqKisjKyrLJhrOSZZnExESOHTtGaGgoERER17w2dXBwoFatWjaPWX+/JUnivffeIyQkhMcee4wVK1bQvXt3BgwYwB9//EFWVhYWi4VXX30VKAtuGI1Gtm/fTq9evfjjjz9sVvy21pS7ml6vVzK0rQFB+L/v6yuzlq116J566imGDx+OWq1WAopXnmdUJoPJepPSmuVs/b62jtX9mLzwbygoKGD9+vWYTCb27dvHoEGDlP2+f/9+YmJiaNeuHY6Ojvz111/8/ffftG3bFkmSSEhIwGAw0KpVKzw9PTlw4ADbtm2jW7duqFSqcgE8lUpFQECAzTEryzJnzpzBYDDQrVs3unXrRmRkJMOGDaOwsFC5kRkZGUn9+vV5/PHHefTRRzl9+vQNz201Gg3vvfcew4cPB8qOKycnJwIDA4mNjeX8+fOEhYVx5swZ9Ho9DRo0uGZbDg4O/2p8prS0lLi4OMxms3JT1NnZucqcN9x2wC47O5v58+fz6quvVpjp5eHhgZ2dHYsXL+aTTz653be7I6rKzrmSTqfj2WefRaPRkJmZybBhw0Rm3V3i5uZWpQNe1gsnQRAqplKpiImJ4eLFi3Tq1KncjajU1FQ2b95M+/bt8fX1rXT6vr+/P15eXmzfvp02bdrw+++/ExQUhK+vLzExMXh4eODi4kJCQgJ2dnao1WoOHz6Mg4NDue97jUaDs7MzsixjMBgwGAzY29srJ9PWoNGVKrrBc3XtU2tA7kpXv3dFbVf0e1TZtmvUqKEsnqLRaG6p3xUtJnX1ftPpdJVq++rXVdR2Zcbkev22ToOdOXMm3377Lenp6UpmxsaNG3nuuefo3r07s2fPVlaG9/Ly4uWXX1aCWNdq26qy+/Lqc6I7eZxUtm1ZlpXjQK/Xl7uAvnqfXN22WDBJEKqnzp07U6dOHXbt2sXEiRPx9PTkwoUL1KhRg27dulX4mm3btvHMM8/w1ltvMW3atGu2ffz4cYYNGwaUfec89dRTSg0ua8AqODiYF198kddee4158+bRrl071q1bh0qlYu7cucqNlJ07d/L++++zdu1aHn/88esGLyrDx8cHvV7Pzp07efXVV+nbty89evTg22+/Zc2aNeTm5mJvb8/Zs2d54IEHaNu2rc3r74UEmurg0qVL7Nmzh+DgYL744gscHR0B+Pzzz1m7di3bt2/nmWee4Y033uCzzz5j0KBBPPjgg6jVao4cOcKnn35K586dGTNmDBMmTGDo0KE8+OCDGAwGjh8/zqhRo+jUqRMAhYWFTJkyRbm5W7t2bYYNG8bPP//M4sWLqV+/Pq6uruzbtw9AKVX27LPP4uTkRO3atcnIyODUqVOEhYVRt25dLBYLc+fOZceOHUpG2tixYwkJCWHGjBkEBQURFBSkfF6LxcIzzzzDO++8w8svv4yvry8HDx6kZ8+eSk2+yli9ejU///wzBw4coLS0lFmzZrFu3TomTJhQbtuMjAzeeecdzp07R0lJCcePH+f555+nY8eOvPjii0q/vvjiC/bs2cO+ffuQJIm+ffui0+nYunUr3377Lfv27VNW7N29ezdjxoxRbrLebbcdsMvLyyM9PZ1WrVpV+I9fp9PRrl07Zs2apdzVvdusJ79VjVarpVu3bhQXF4tg3V2UmJiIwWBQMgCqGovFYlMPSBAEW/n5+UydOpWGDRtWWJDawcGBFStWkJqayhtvvFHpdj08PHj99deZOXMmS5YswWKx8M4772CxWHjttdd4+eWX6dWrF59//jmJiYkAFBUV8c477yhTIu5Ht7LiblUmyzIxMTGMHDmSzZs3l/s+TklJYdq0aSxatIiUlBTg/y4Wr6x1eL+pbseBIAg3x9PTk4YNGyqZZACBgYEsXbqU+fPnc+DAAdLT04mIiGDAgAFAWTZegwYNCAgIUIL6bm5uNGzY0Cab/Uqurq40bNgQgL179wJlNxY6dOigFOG3TnOVJInevXvz22+/kZWVxdatW7FYLHTq1Il+/fopfQ0ICGDTpk1kZWWRk5NTLvvPwcGBhg0bUrt2bSRJwsnJifr16xMUFIQkSdjb21OvXj0leyo8PJxx48axZs0ajhw5QtOmTenZsydLlizhm2++4cSJE2g0Gho0aEC/fv1Qq9UEBgbSsGFDm5uMjo6ONGjQgODgYKXu/JVj7OfnR/369fHw8ECSJAICAmjYsKFNlqNwbSdOnCAkJISuXbvSsWNHdDodsiyTl5fHxYsXOXXqFFqtlnHjxtG4cWOWL1/O+fPn0el0dOvWjQYNGqDRaBgxYgRRUVH8/PPPnDlzBrVaTdu2bWndurWS9W8tjXHu3DmgLN5hMpno06cPBQUFHD9+nISEBAIDAxk2bBjPPfccarWaN954gy1btnD+/HnMZjNPPfUUw4YNw9vbG1mWiY+P5/z580pgLiEhAaPRWGHNc5VKxUsvvYS9vT3Lly8nIyODN998k+HDh1d4zDg5OdGgQQPlOLdKS0vj9OnTeHh44OHhQV5eHqdOnaK4uBg7Ozvq1atHQEAAUJY9d+bMGXJycqhXrx5QVjbGGiyHshuvzzzzDCdOnCAgIIDXX3+dgQMHApCVlcXJkyeVvhiNRk6ePHlPTfuW5NvMaY2JiWHAgAGsX7/+mgGMbdu2MWHCBLZt23ZX74DKssy8efPw9fWlT58+Ve7EUJZl4uLiKCoqIjw8vMr1/35gzYCwt7cnICCgSu6D4uJihg0bxvTp0yucLiAI1V1cXBw9evRg5cqVhIWFlXtelmV++eUXlixZwurVq2/qd81isZCTk6PUdnVyckKSJDIzM3FwcECv15OXl0dRUZGSPefs7HzN97D+roWEhNClS5cq951kPRksLi6mVq1aVa7/N0uWZfbs2cOoUaPYv3+/kqXh4OBAaGgoly5dUurVXalRo0asXr2awMDA+3KMbvc4MJlMDB8+nEmTJonfNUG4D1m/K69M/rh6euaVl7RXfodYH7/6savbufK5ii6Pr36/K6epXvnY1c9f3WZFteOufr6ifl/rc1zdl8pue733vfqzXeuzC9d39fhd73i4+ri73muufNz6XEWutU1Fx19F73u9BZ2udwxUdFxVtO21/l1U9t+gSqW6bh9VKhV9+/Zl8+bN/PLLL3Tt2rXSn/FeOsZvO8POYDBgMplITU2tMGBnsViIiYnB3d39dt9KQNQfuxdY6+UIgnB/shblt05duJokSXh7e5Obm3vTbatUqgoXX7oyg87Z2bla3b2+csXP+5XFYqGwsJDt27fz9ttvc+rUKaDsWAoMDFRWLDx9+jSTJk1SFlSAskLS48aNu2+DdVbV4TgQBOHWXO+i/3rP3eg119r+Rs9fb/trvd/NvueN/n69bSr7mW/0vpX5bMK1XWu/X2vcb/W4rMx+uZljurJ9ul57le3Trf57qey2rq6uyhTyiq7f76XA3LXcdsDOWnh72rRpzJgxQylCbHXixAm+/vprhg0bds8MRlVeMMDDw6PcCjHCf8vX17dK14Cz1okSBKFiGo2G0tJSZWWrq8myTGZmZoW1zYSbZ1204H6Vnp7O2rVrWbduHZs2bbIp5NyyZUvmzJmDr68vRqORBx54gPr16/P9999z8eJFLBYL/fr1q3Bq9v3mfj8OBEEQBEH4b3355ZeYTKYqvVDnHcmwGz16NMOGDaN79+507NiR4OBgTCYTx48fZ8+ePbRt25a+ffveif7eEVV5ZRuLxSLuQN9lFoulSmfYVeXjXxD+Cy4uLgQHB7Ns2TJGjhyJwWCwScNPT0/n559/vmbtVuHmeHl53ZffSyaTifXr1/Pxxx9z7NgxioqKlOfUajVdunRh2rRp1K1bF5PJhCzLqFQqWrVqRZMmTSgoKMBiseDq6lotbrLcr8eBIAiCIAh3R1UO1Fnd9hmgtfjmN998w08//cTmzZtZvXo1arWaiIgI3nzzTQYMGICLi8ud6O8dUVpaere7cMsyMzMpKiqqVtOl7jVJSUnY29tX2YU/xKITgnB9Li4uvPbaa4wYMYLY2Fh69uyJv78/ZrOZM2fO8OuvvxIXF8eUKVPudlfvC3l5eRiNxnIZ+lWZyWRi3bp1vPrqqyQnJ9s8ZzAYeOKJJ/j444+Vz3zlGEiSVOEqrPe7+/E4EARBEARBuB135JatJEkEBwfz1ltvMXLkSGXKplarveZ84cowGo38/fffJCcnExwcTKNGjWymS8iyzOXLlzlx4gRFRUV4enrSrFkzEcwSBEEQbpkkSbRv357Zs2cze/ZsRo0aRWFhoc3Kbd9//z2hoaEiw+4OyM/Pp6io6L4J1BQVFbF8+XLefvttZZVXlUpFaGioMuOgXbt2ODg4KAWP77cxuBViDARBEARBEGzd0TkWGo3mjk3bMJvN/PDDD2zdupUmTZqwZMkSnn/+eXr27GlzgXTs2DH++ecfnJ2d+eOPP1i/fj0TJkzAwcHhmm1X5QssjUZTpWvw3Q/0en2VrrNTFYprCsLdptFo6NKlCy1btuT8+fNkZ2cjSRI+Pj6EhYXdFyn2wp0lyzJ5eXnMnDmTmTNnkpWVBZTdvHziiSd46623qFu3bpWugSoIgiAIgiD8d+7ZoiiJiYksW7aM2bNnU6dOHerVq8eCBQvo0KGDTTCuV69eShDvwoULPPHEE+Tk5Fw3YFeVC4XXqFHjbneh2gsODr7bXbgtarW6SgccBeG/5OrqSrNmze52N+5rjo6OVX76pyzLxMXF8e677/Lzzz8rpTe0Wi1PPvkks2bNwsXF5Zo3S+6HMbhdYgwEQRAEQRBs3XbATpZlMjIySElJwd/f/7onpDcjLS0Ns9lMUFAQkiTRqFEjcnNzSUxMpHbt2sD/ZcrFx8dz/vx5/vzzT9q1a1euXp4sy1gsFuX/RUVFyrRdtVqtPG5tU6VS2azEap3Se+ViD2q1WmnzWq+7U22rVCokScJsNpOWlobRaCQgIKDCtq9+3Z3s97X69G+0fTNjcqfbvl6/zWYzCQkJGAwGfHx8bGrB/dvjfafaNplMVbqOoyAI9xcnJ6cqvdiAddXg119/nXXr1infwY6OjgwbNoy33377hudGVX0M7gQxBoIgCIIgCLZuO2CXk5PDN998g6OjIxaLhVdeeeWOTNksLS1Fo9EoJ7j29vZYLBaKi4vLbXvx4kX++OMPTp48yYMPPlhuusnZs2f55ptvKCgo4OTJk/Tt25ejR4+i1WoJDQ2loKCA+Ph4oOyEsVatWkRHRysruoWEhKBWq7lw4QIWiwW9Xk9ERATx8fFkZmYC4Ovri7u7O+fPn8doNKLRaAgLCyvXdnh4OGfOnFE+R0hICCqVikuXLmE2m7GzsyM0NJTk5GQyMjKQJAlvb2+8vb05deoUeXl5yLKMp6cnubm5JCQkAODs7ExwcDAxMTEUFBQodQXVajUXL15U2g4LCyMpKYmMjAwAfHx88PHx4eTJk5jNZiRJIioqiuzsbBITE5FlGRcXFwICArh06RL5+fmoVCoCAgIwGAxER0cDoNPpqFOnDnFxccqY+Pj44OXlRXR0NKWlpajVasLDw8nLyyMhIUFpOzAwkIsXL5Kfn48kSQQGBqLX6zl79ixQlqFQr149Ll++bNO2u7s7Fy5coKSkBI1GQ2hoKEVFRcTFxZXbl4WFhUBZdpxWqyUmJuaa+9LHxwcPD49y+7KwsJC4uDhyc3NxcXHBx8fH5jgJDg5Go9HYjHdoaCgpKSmkp6cD4O3tjY+PD6dPn8ZkMqFSqYiIiCAvL8/mOAkNDeX8+fMUFBTYHIPWtg0GA2FhYTZte3l54efnxz///KPsyzp16ijHiSzLODs74+vri9FovJl/joIgCP+a1NRUiouLCQ0NvdtduWmyLFNQUMDEiRNZv369Eqzz9vZm/PjxPPvss+j1+hveyKzKY3CniDEQBEEQBEGwddsBu4KCAgoLC+nRowdLlizBZDLdkYCdwWDAaDQqmUHZ2dmo1WocHR3Lbfvggw/SunVr4uPjefLJJ+nbty/h4eHK88HBwYwaNQqz2czixYvx9vamdu3aSJKEnZ0dWq1WqUekVqtRqVQEBgYq721nZ4ckSYSFhQFl2U4ajQZfX188PDyAsoCVTqcjODgYWZYrbNta3y8oKKhc29YTVJVKhU6nUwJS1ra1Wi1hYWEkJiZiNBrR6/W4ubkpK5Va6wcGBAQoFwwVta3Vasu1bQ12Wel0unJta7VaatasadO2SqWyyXZUq9XlxkSr1RISEqLcNdfr9Wg0Gpsx0Wg05dqWJMmmbZVKdc3xtlgsSJKEvb09er1emfJ85b68uu1b2Zc6nQ69Xs/ly5eVKdcVHSdX70tvb2/c3NxsxiQ0NPSaY6JWq1Gr1TfclxW1rVarbfal9Ti5crwlSbpjtSYF4X4kyzJJSUnExsYSFhaGp6enqPv4LzKbzVVy5WpZlklPT2fKlCnMmzdPyVwOCwtj7ty5dOzYsdLftVV1DO4kMQaCIAiCIAi2bvuq3cvLixYtWrB582Y6d+58x+qP+Pr64urqyqFDh2jevDk7d+6kRo0a1KhRg7S0NBwcHLCzsyM7OxutVotarVamul59YaXX6/H19VUyupydnXF2dla202g05eraOTk5levT1VNtrQGtK129Qu2dbNvJyYkaNWpgMplQq9UVtl1RQLMybV+9jVqtvmNtVzQmVx8nt9r21WN5J9uuqN86nY6aNWsqNeDu5HFyp/pd0b68sm1r1qAgCBXLyspi0aJF+Pv7s3v3bl577TVRW+tfZL0pU9Xk5OQwevRoli1bpmQtu7i4MHXqVDp16nRTi0tU1TG4k8QYCIIgCIIg2Lrtq3atVsujjz6KxWK5oyda3t7evPLKK3z++ed4enqSnp7OuHHjkGWZp59+mpdffpnevXvz2WefkZSUhEajITU1lSeffJKAgIDrtl2VTwgNBoNNHTPhv2dvb1+ljyFxUSQI11dUVERRURENGjTgl19+sakLKdx5Xl5eVe53LT8/n0mTJrF06VIlK8zd3Z3x48fTp0+fm14JtiqOwZ0mxkAQBEEQBMHWbQfsrFlqN3tyWpl2O3fuTL169SgoKFBqhgHMmzcPd3d3JEli5MiRZGVlYTabsbe3x9fX94aZECUlJXe0r/+ltLQ0iouLlSmdwn8vISEBOzs7/Pz87nZXbonZbBaLTgjCdXh7e9OhQwf27t1Lnz59qvTK4lVBaWkpJpNJmbp/rzOZTHz77bfMmzdPCdb5+PgwY8YMHnvssVtahbuqjcG/QYyBIAiCIAiCrXt6Xpy1jtfVgoODlT97eXnh5eX1H/bq7rJYLCLb4y4zmUxiHwjCfUyj0dCuXTvatWt3t7tSLWRlZVFUVISrq+vd7soNybLMjh07mDJlirIokIuLCzNmzOCJJ5645XIDVWkM/i1iDAShajOZTFy4cIHExES0Wi3e3t6EhISg0WgwmUycP3+e5ORkpVZ3QEAAR48eJTc3t1xbBoOBFi1aoFarOXLkCPb29tSpU+em6skWFhZy/PhxmjdvfscTS4T7m9ls5uLFiyQkJKBWq/Hy8iIkJASdTlfhcR4UFMSJEyfIysoq15ZWq6Vly5bodDpl0ct69erd1LFcUlLC4cOHad68uShrVA3d9h63WCyUlpZWqr6PLMsUFxff9bunVbl4uLV2nXD3WGsmVmVV+d+AIPzbxL8P4VrOnTvH6NGjSUlJAcoWBBo7diwDBgwQv82CIFRbZrOZhQsXsnjxYoKDg1Gr1WRmZjJnzhwCAwP56quvWL58OYGBgahUKgoLC5k2bRpbtmwhNjaWvLw8Nm3aRNu2bfHy8sLV1ZWGDRuSk5PD448/TlhYGCtXrixXp/l6EhMTefHFF9m/f/9dv/YUqg6LxcLSpUv5+uuvCQwMRKfTkZaWxowZMwgPD2fRokV8//33BAUFodFoyMnJYdq0afz111+cO3eOgoICNm3aRKtWrahRowYODg40aNCAvLw8nnrqKVxdXVm/fr2y2GFlpKen8/zzz7Nv376b+jcg3B9u++wyJSWFiRMnMmTIECIiInB0dEStVisXPNYgXXZ2Nlu3buXChQt88MEHt93x23EnVrG9Wzw9PUWNl7vMz8+vSteAE0FfQRDuJVXhN1mWZXJzc5k0aRJHjhwByurhPv7447z88su3/Z1aFcbg3ybGQBCqrtTUVBYtWsSYMWPo06cPUFbGx9nZmeTkZBYtWsSUKVPo0qWLssK2s7Mz48aNAyA2NpYzZ87wzjvv0KRJE6Xd5cuXExYWRkJCAtHR0bRo0aLce8uyTFFREcnJyZhMJv4fe2cdZkd1/vHPGbu27nHZkBB3DwTiBgSKuxUtpYRSihQKJTgVCpTghfIrVooTXBISAlHi7tmsy/Wx8/tjdi9ZEjwhSbmf57lPcmfnzpx558zMOd95JSMjg6Kiot3Wqauro76+ntatWwNQVlZGLBYjJyeHoqIipJRs2bKF4uJiAoEAVVVVhMNh2rdvj+u6bNu2jaKiorT49z9OTU0NDz/8MBdeeCGnnXYaQgiqq6sJBoNUVVXx6KOP8rvf/Y5jjz0W8MS0UCjEtGnTAK9frVmzhquuuorhw4entvvCCy9QXFxMVVUVS5cu3WMUh5SSZDJJWVkZpmmSkZGRSgm26zoNDQ3U1NTQunVrhBDs3LmTaDRKVlYWxcXFCCHYsmULBQUFhEIhampqqK2tpUOHDgBs27aN/Px8QqHQPrJimr3Jj561Z2Vl0b17d6ZNm4bP52Pw4MF06NCBrKwsLMuiqqqKZcuWsWDBArp06cKll166N9r9oziYwxnj8Ti2badzKu1HIpEIuq4ftJMLKWVa9E2T5htwHAfTNL/ToFxKSSwWSw96fgRfnVgdiDiOw/3338/zzz+fWtavXz9uvfVWcnJyfrRX5sFgg31N2gZp0hy8ND03I5EIDQ0NZGRkUFhYiBACy7KwLItwOEw4HCYUCqX+tiealsdiMd58801OOOEEVq1axcsvv8zAgQN3+10sFuOaa65h5cqV+P1+QqEQd9xxR7N1tm7dyu9//3v69OnDxRdfzBNPPMF///tfDMNASsk111zDkCFDuOqqqzjrrLOYPHkyt9xyC++//z4ffvghtm1zyimn8PTTT6dEjzT/m+ypL+fn5yOEoK6uLtWXm/5WUFDwrX3ZNE1ef/11jj76aGpra/nvf//L4YcfvpsDiGma3HDDDSxcuJBgMIhhGNx2220Eg0HAG3Pu3LmTa6+9lvbt23PFFVfw7LPP8u9//xvDMHAch2nTpjFmzBj+8Ic/MGnSJE466STuuecenn/+eWbNmoXf7+e0005jxowZdOvWbd8aM81e4UcLdqFQiIsuuoijjz6auXPn8tprr/Hxxx9jWRZCCEKhEEOGDOGBBx6ge/fuZGVl7Y12/yiakkQfjDQ0NBCPx7+XG22avUvTW5aD1SU5nQcxTZpvZufOnfz+97/ntNNOo3fv3uTl5WEYRmrg5boukUiEnTt3MnPmTMrLy5k+ffp+bvXBS0VFBclkkrZt2+7vpuwRx3F4/fXX+fOf/0wikQCgTZs2TJ8+nZYtW+6VEOoD3QY/BWkbpElz8FJcXMwJJ5zADTfcwFNPPUXPnj2ZOnUqQ4cOpVWrVhx33HFcffXVPProo/Tq1Ytf/OIXDBgw4BtTzGzevJnVq1dz9913U1payq233kpVVdVuucs3btzI0qVLeeKJJygpKSEWixEIBNi2bRtSStauXcuNN97I6NGjOf/881m0aBH/+c9/eOihh2jbti0zZ87k9ttv5/nnn2fgwIHMnj2bI488ks2bNxMKhVizZg1SSoLB4M8qb/rPlfz8fE455RTuuusuXnjhBXr06MExxxzDYYcdlurnN910E08//XSzfv5Nnvbbt29n4cKF/P73v6empobf/e53lJeX06JFi2brbdu2jc8++4yHH36Ytm3bEo/H8fl81NTUAJ4n6vTp0+nfvz+/+tWvWLNmDU8++SQPPvggpaWlzJo1i9tuu40hQ4YwdOhQZs2axTHHHMO6devIz89n2bJlFBYWoijKbvtOc+CyV+LimopDtGnThhNPPJFEIkFDQwO6rpOZmZkOv0uTJk2aNAcNeXl5jB8/njvvvJNEIkGvXr1o164dOTk5mKZJZWUlq1evToXnXHTRRfu7yQc1pmmmhLADDSklixcv5qqrrqK6uhrwipJcddVVjBkzZq+lRziQbfBTkbZBmjQHL7quc+WVVzJx4kTmzJnD7NmzOfPMM5kxYwZjx47luuuu45hjjuGTTz5h1qxZnH766Tz22GOMGDFijy89pJS89tpr9OrVK5XPLhqN8sUXXzB69Ohm65aUlODz+bjtttsYOnQoI0eOTL1Ur62t5ZJLLuG0007j3HPPxTAMPv/8c2KxGC+//DJCCBoaGli/fj3RaJQ+ffrw4IMPsnz5cnRdZ/z48XzxxReEw2H69u2bjnD6GaBpGpdccgmjR49mzpw5fPLJJ5x77rn89a9/ZerUqUybNo2JEyfyySefMHv2bM466yweeOABxo8f/7V9+e2336a0tJSioqKUYPb5559z1FFHNftNYWEh2dnZ3H777YwYMYLDDz885dEZjUa55JJLOPbYY7n00kvx+XwsXLiQcDjMG2+8gRCCWCzGpk2bqKuro1evXrz22musXLkS0zSZOnUqixcvJjMzk27duqUjQw4i9omS5vf7D+gb2sGcfywQCBz0BQ8OdjIzMw/acFjw3LMP5msgTZp9TSAQ4NRTT2XixIksXLiQN954g9mzZxOPx1EUhZycHIYMGcIf//hHOnTokM5n8z9MfX09119/PWvXrgW8SekFF1zAWWedlb6PpkmTJk0jUkp0XadPnz707t2bM844g8suu4zXXnuNsWPHous6/fr1o2/fvpx55pmce+65vP322wwfPnyPIkdDQwNvvPEGmzZtYsqUKUgp2bhxIy+++CKjRo1q9pv8/HyeeOIJPvzwQ2bPns2DDz7IjBkzCAaD6LpOSUkJa9aswbIsDMNIpbxoEiyCwSBXX301oVCInj17snPnTmbNmkW7du0YNWoUDz30EEIIJk2alJ6D/QyQUqJpGj169KB79+6cfvrpXH311bz00kscc8wx6LpO79696dWrF2eeeSaXXXYZb7zxBuPGjdtjX47FYrz66qssWbKEo48+GiEEa9eu5T//+Q9Tpkxp9pvMzEwefvhhPvroI2bPns2MGTP4+9//TsuWLVFVlVatWrFmzRoSiQQ+ny/Vl4PBIEIIgsEgv//978nJySErK4twOMzHH39MixYtGD16NHfccQe5ubkMGDAAXdd/SrOm+RH8LF3fDmaxJS8vDynl/m7Gz5qmZJ4HK6qqpgccadJ8C4qikJ+fz9ixYxk7dizJZJJoNIqmaYRCofQ1tBfJzc09INJlfBXLsnjkkUf44IMPUsuOPPJI/vCHP+z19h6oNvgpSdsgTZqDl3g8zpYtWygtLU1FVsViMdq0aUM0GmX79u107NgRTdNwXZdEIkFmZubXjqdXrVpFRUUFzz33XOq+sHz5cu655x527NhBq1atUuvGYjGCwSAnnXQSxxxzDMcccwzLli1j0KBBZGZmcs8993D99ddz8803c+ONN9K3b1/eeustpk6dSosWLXBdl5qaGkKhEH6/n7y8PJ5++mn+8Ic/0L59e9auXYvjOFx//fUH9fg/zXcjmUyyceNGOnbsmNIMIpEI2dnZqX7esWNHdF3HdV1isRitWrX62r6xYcMG1q9fz7///e9USPW6deu4+eab2bJlC+3atUv9Nh6PYxgGxx9/PEcddRSnnHIKixYtomXLlvj9fm6//XZuvfVW/vCHPzB9+nR69eqFYRgcddRRtGnTBikl1dXVZGVlIYSgRYsWPPnkk0ybNo3WrVuzY8cO1q1bxyWXXJLuywcRP0vBLplM7u8m/GDKy8tJJpO0b99+fzflZ8uWLVsIBAKUlJTs76b8IBzHOajzOKZJsz/w+Xz4fL793Yz/STRNOyC91b744gvuvvvu1JihQ4cO3HrrrfukOMKBaoOfkrQN0qQ5eGloaGD69OlEIhFycnIoLy9HCMEZZ5xBfX09N910E4lEguzsbMrKyvD7/Zx44ol7FA1c1+XFF19k5MiR9OnTJ+UJ1Lp1a+69917mzZvHsccem/rt+vXr+dOf/kQwGCSZTKLrOgMGDEhtr6ioiLvvvpsrr7ySW265hd/+9reMGDGCs88+m5KSEpLJJO3atePmm2/GMAyGDRvGfffdx6GHHkpRURHFxcVUVFSk82v+TIhEItx1113U1NSQm5tLZWUlpmkybdo0wuEwt912Gw0NDal+DnDGGWd8bV9+5ZVXGDhwIAMGDEhFILZt25b7778/5cnZxLZt27jhhhswDAPbtonH4wwbNiz197y8PO644w6uvvpqbrjhBq6//nomTZrEeeedR0lJCZZlUVxczPTp0wmFQowYMYIPP/yQHj16kJeXR/v27Vm6dGm6cMpBhpA/I3ctKSUPPvggJSUlTJ069aBTlqWUbN26lXg8TufOnQ+69v8vIKVk9erVBINB2rRpc1Ceg0QiwcUXX8ydd96ZTp6bJs1BTtNzrUOHDl+bP+VARkrJ9u3bSSQSlJaWHjDtTyQSXHjhhTz55JOAl+rj7rvv5sILL9zreXkPVBv8lPxYG9i2zaWXXsott9ySfq6lSbMfcF2Xqqoqtm3bRiQSIRAIUFpaSm5uLlJKKisr2bZtG9FolGAwSKdOncjOzk5d64lEgmXLltGlSxdCoRDLly8nJyeH1q1bp9aRUrJy5UpCoRBt27ZtVoFz48aNVFVVoaoqHTt2pKioiHg8ztKlS+nfvz+KolBdXc369evp2bMnmqaxbt06qqqqCAaDtG/fPlXQr7y8nC1bttC7d298Ph+rVq3Ctm26deuWfqnwM6DJS23r1q2Ew2F8Ph+lpaWp/lFVVcXWrVt36+dN/TGZTLJs2TJKS0vJyspi9erV+P1+2rdv3+zZtnr1anRdp0OHDqnllmWxadMmKioqUBSFDh06UFxcjGmaLF68mP79+6OqKnV1daxevZoePXrg8/lYt24dlZWVBAIB2rZtm3qxWF1dzbp16+jTpw9+v59169YRjUbp0aNHOlLkIGKvjjqllEgpm91YHcdBCIGqqj/LQei+IG3H/YsQIn0O0qRJk2YvcaBVrpZS8sEHH/D666+nlh1++OGcddZZ+6yI1oFmg/1B2gZp0hy8KIpCUVHRHj2QhRAUFxdTXFz8tb/3+/3NvOJ69uy5x+1069Ztt+WGYdClSxe6dOnSbHkgEGDQoEGp7wUFBRQUFKS+72lb4BWx2DWK5tBDD/3adqf530MIsVtf2ZXCwsJvfDHk8/no379/6nvXrl33uN5X+yt4eXIPOeQQDjnkkN22OXjw4NT33NxchgwZ0mwfe9rPV4+jU6dOX9vuNAcue/U1werVq3n11VcxTRPXdXnkkUcYNWoUv/zlL9m0adMBk3vtYM5hV1BQkC7DvJ9p2bJl6i3LwYiqqunKzWnSpDlg0DTtgHouV1RUcOedd6aqwhYUFPCb3/xmn1ZUO9BssD9I2yBNmjRp0qRJk6Y5e3XW/uKLL1JdXc2UKVNYuXIlDzzwAJMnT2bRokU8/PDDTJ8+fW/uLk2aNGnSpNmn1NXVoSgKfr8fXdfT3rX7gIKCAlzX3d/NQEpJIpHgr3/9K7NmzUotnzhxIkccccQ+PfcHig32J2kbpElz8NIUZZUmzd6i6Zn7U4+70n05zb7gx0To7VXBrqysjNLSUhRFYdasWXTq1IkbbriB9957j7/+9a/NwmX3J6Zp7u8m/GCqqqpIJBK7ucqm+enYsWMHwWCQ1q1b7++m/CDSRSfSpPnu3HzzzcyaNYvBgwczYMAAunXrRpcuXVIVuNL8eCKRCJZl7ZNiDt+XF154gQceeCAVmtmpUyeuvvpqAoHAPt3vgWSD/UXaBmnSHJy4rstTTz3FunXr8Pv9Pyo3lmmazTxtm9Ir7a/IkK+2Z2/xQ+fEruviuu4+t4eUEtd193guTdPEcRx0XUdVVSzLQtM0hBDYto1pmgghUqKXruvoup6KwPum56lt2ySTSRRFIRAIcNZZZ/2kkWVSSp555hlWrFiBYRipgic/hD31Hdu20325kaY0GD/Gxt+Vr7O7bdup/quqamo9IQSO42CaZjPxVlVVfD5f6neBQOBrj72pGjZ4If+nn376Dy5cs1d7TEZGBlVVVTQ0NPDZZ58xZMgQfD4fhmGkleq9SNqW+5f0m5c0aX4+XH755QwfPpzZs2fz6KOPYpomgUCALl26MHr0aE444YS0cPcjiUQixOPx/SrUSClZunQpt9xyCw0NDYCXI+amm276SfIXHQg22N+kbZAmzcGJ67r85z//4YsvvuCPf/wjRxxxxA/ezowZMzjttNNSQlEkEmHJkiUMGTLkJ0+S39SeU089da+KLFJK6urqyM7O/t5FLFavXs2GDRuYOHHiXmvPnohEImzfvn2PedbeeecdnnzySfLz8zn22GOZO3cuU6dOJRAIsHLlSt599102bdpEYWEhmZmZ9OvXj6FDh/Loo4+ydu1aHnnkka9NMTFr1iwuvfRSAoEARx11FFu2bPlJBTvHcXjppZeYO3cu559/PieffPIP2o7rujz00EOccsopKUHKsizmzJnDwIEDCQaDe7PZ36k9Dz/8MCeddBI+n2+vbru+vp5QKPS9r5HNmzezcOFCjj322L3anq+STCZZt24d3bp12228vmDBAu6//378fj9Tpkxh+fLlTJgwgaysLHbu3Mnrr7/OypUrycnJIScnhw4dOjBy5Eg+/PBDHn/8cZ5//vlm+S53Ze3atZx11lnU19czadIkBg8efGAIdqNHj2batGls3ryZBQsWMG3aNFzXZdOmTRQUFBwwk5oDpR0/hHSOl/3Pj33jsr9JF81Ik+a707ZtW9q2bcuxxx5LIpFg5syZPPbYY/zzn//k448/5vjjj09fT/8D1NXVcd1117F27VrAS6B+3nnnceKJJ6YrqaVJkybNt9C6dWsKCwuxbZt27dr9YI+bc845hw4dOqSELNu2KS0txe/375fQyHPOOYeOHTseMNVhc3Nz6d69O61atdrn9ujRo8cel59zzjlMmTKF2bNn89xzz+H3+9m5cycDBgxgzJgxtG/fnrVr13LEEUfg8/lS8478/HyCwSC1tbUEg8FUP9n1OEpKSujYsSN1dXVs3LhxvzhIFBUVcd555xGLxWjduvUPEmt37TtNYwgpJcXFxYRCoZ98XCGl5Oyzz6ZTp04HzJimoKCA9u3bN6v4vK/Yk/AM3hj/8MMPZ/HixTzxxBOYpsnWrVsZOnQorVq1onXr1syfP5/DDjuMjIyMVH/Nzc2lT58+uK5LNBqla9euu4Vwt2rVijfeeINNmzZRXl7+o/ryXhXsDj/8cKZPn86iRYs477zz6Nq1K67rYhgGZ5xxxt7c1Y9ibyvLPyXFxcVp7679zE9xY9mXpItOpEnz3dm4cSMLFixg7ty5LF26FPAGc7fccguHH374QX0vOFAIhUL79SWIlJLnnnuOmTNnpkI7Jk6cyBVXXPGTDWz3tw0OBNI2SJPm4KWsrIy6ujouvfTSH7wN13V55plnuO6661ICWX19PUuXLt0vz1vHcXjmmWe49tprDxhniY0bN7Jy5cof7Pm1N9A0jRYtWnD88cczcuRIrrzySj799FNee+01QqEQO3fupKKigoqKCk4++WT8fj/19fV8/PHHGIbBJ598gq7rFBUVcfnllzNhwoTUufX7/QwfPhwpJU899dR+Ob76+nrWrFnD5Zdf/oPHAE19+fe//31qG4lEgjlz5nDkkUfuF2/RZ599lquuumqfp/j4ruzYsYNZs2Zx7rnn7rc2KIpCYWEhY8aMYcCAAVxzzTWsXLmS999/n1AoRFVVFZWVlaxdu5bzzz+fjIwMTNPkww8/ZMOGDVxxxRUkk0ny8vK44IILOP7441NzbF3X6dOnD7179+a///3vj2rnXp21a5rGxIkTmTJlCuC5fkajUY477jiCweABM7E5mHPY1dTUYJomLVu23N9N+dlSXl6OYRhfW+77QMdxnFR+pjRp0nwz9957L48//jhTpkzh4osvpnv37nTo0OGAGbz/L5Cbm7vfXkRJKVm/fj3/+Mc/Urk9i4uLuemmm2jRosVPNm7ZnzY4UEjbIE2ag5fq6mo2btzIunVryc7MoCQvF03XoVGYUFQNhIJEIJDA7te64zjYloVpmpiWje26bNiyjc8Wf0Hnnn1Q1W/2clOEQFW8e7auKDhS4n7NPcV2d01vIwjoKupX7ve245A0TWLxBJb95bhZUxWkBOcbiuR4bVFwpfzG9ZoQAlRFwXZddOWrYo5EIJEILMvCthrzxEGjLQWSL9tuuy6KEChC4EiXhGVjOp7NNUVBCBAIDE1pti/LdfZ0WnZDU5XGsygJBEN06NiRCy68iLKKStZt20lmfkuEdHj2nzN4/PHHmTp1Kjk5Odxyyy1YloWiKKiqypw5c7j22mvp0a0rxYWFSOmC4yAb22HFIt/emH1AdXU18+fPZ8OGDaxevZr27dvj9/txXRfb+m45wB3XwbEs7GQSpdGokYZ6vli8iKGDB6WW7Yrk2704BT+sMJPruF57zCSWsm/HNdJxvHP5LZixCFY8hhmNkOq+EqRjASC0L1/gCUVFKN79w/3KtWe7Ls5XrnPbkUgkbuNiRYCqCHRFRVM8Lzkt5TXrXV8hv592bdtyzrnnYts227dvp7CwkEAgwFNPPcV9993HaaedRkFBAZdddhn19fX4fD4URWHZsmVce+21dOrUif79+2ObpncwrovrONjJBJD1Ay26lwW7xYsXs3TpUk466SQ0TeOee+7hqaeeomPHjtx5553N3AX3JwdzFbJ4PE48Ht/fzfhZEw6Hf/LcA3uTpkSyadKk+XYmTJhAPB5n/fr13HXXXRx66KEMHDiQHj160KVLF4qKig6I59rBzI4dO0gkEpSWlv7k+7ZtmwcffDDlPanrOr/61a/o3bv3T3pe96cNDhTSNkiTZt+wc+dOZsyYwcaNG7nooosYMmQIjuPwxhtv8NFHH5Gfn88555xDcXEx69at46mnnqJDhw6ccsopbNmyhenTp3P55ZfTt29ftm3bxr///W8uv/zy3SKWtm3bxqmnnsYhHTtw//VX0bFNS/ScAhTDh5GZg1RUbKGiIBHSxRUqqvxSCLMRjB07lq1l5USSFlsqqnj+//5Fv9GTeHXBij0em4DUBFwRkO3XMVQFQ1VxpMRy3Gaine1KEpaD6biYrqQhaZMX0GmVFaAg5EcVAtv1xC0pYcDQEWzcXtYsJFZXVaSU2N8i2GUFfMRMC9N2vPzXjcubcF2J2yiiaYpCdtCP47roqtr8+SMlqnRwhEpmZibdunWjuqoaDRdVOrgo2Io3pbcdl9pYHLVRsItbNhWRGOWRBK4EXfWEigxDoyDkpzAzRED3fms5DrGkhSsliiII6DpJ2xNOg4bRKCdJDFUFIQgnTGKmSd9BQ1m1vZyV1THmV6h8saiMggwYdOQxRJbM5u677yEzI0jbdu3Jy8tj7NixhEIhDMNg+bJlyESMmk3rcC0TJxbBiTaAENRvXg99+jWza3V1NQ8++CBr1qzhnHPO4YgjjsBxHN59913effddsrOzOfvss2nVqhWbN2/mn//8Jy1atOD000+noqKCG2+8MXUNVFZW8sgjj/Cb3/ym2bzOdV1qamq4/PLLadWqFQ888ACTJ09GSkk4HMZMJhuFUpr132ZIycTRRxKrrSapKiSTSR56/AnGjxmNEw0Tjoabr06TbNT0XeAKpdn2mwTaHzIycSVMGHUk8bpako0i+le31dQGR3wp4grpouJ+r306ZhI7Fm0UXiVCVWEPW/AnY/Q7pAM1m9Y2+7u0PMcqoTe+GBcCPZSJ1AxsoRKzbCKJJLbrEjVt4pbdKIx7An3UtEnaLqbr0pB0kEC+XyPLr5MbMMgL+gkaOlkBf+MOJZr02nr4YYfj2A4FBYXkFxSm2nTZZZfx3HPP8eCDD6IoCq1ataKwsJCRI0dSWFhIUVERn3zyCa1atcJOxKnZtM675l0H17ZpKNtGXuEPz8+7VwPyX331VVauXIlhGCxatIjnn3+eSy65hOzsbGbMmLE3d5UmTZo0adLsc8aPH899992XervWt29fXn31VU4//XTOPffctEfQXqCpUthPjZSS+fPn889//jP1EqNPnz6cccYZ+yVc5efu+Zy2QZo0+wZd1xkwYABCiFQupeXLl/Pvf/+b888/n8LCQv7617/iOA6vvvoq48aNY/v27akw17lz5/Lwww+nIqcWL16827Uq8DzEkJINm7fwjxdeoTpugRAIoUCjSKfZSVTbREGiSBdLaKmPLeG1N96kIZ6gJpYgaru06tGfmBGiLGqyPeJ9dkZNymMW5TGLqOXdu/2aQqZPx5UgJViuS9J2qImZVO/yqU9YJB0XXVXwawqGKohbDpXRBFXRBHXxJFXRODvDcXbUR3jtzZlUhKNURePUxhLUxhJURGLELAvbdZt9IknLWyeexHQc6mIJzEbPvKhpUxdPNhMPXTyRATwvIdN2UmKdbPTMaxpjOEJFwWXH9h0sXLjQW4aCLTQcoaA0Cg5NhfFs1yVmWlREYql9ACRtl3DSZmckwabaCOUNEZK2jQCCChQEdPJDfnICfnyaSqbfR24wQFAThFRJhioxsNGkg19XUQTMfOcdvthexbLKONvrXXQVSvM1OhRkc/Kpp3Lvn+/mrF8cixuu5f/+9S/efPNNHMdh27Zt1FVVoEbqsONRpOuiBEJoWblo2fmooczd+rKmafTt25dgMMj27duRUrJu3Toee+wxzj77bDp06MBdd92FZVnMnDmT4cOHU19fz8aNGwmHw8ybN48ZM2aQSCSIx+MsXLgw5V2f6stCoCgKruuyY8cOHnjgAZYsWYKiKGRmhDCkhS5tdGmn+rHi2IhdPjg2b779NrbjbVtVVYYMGkj7ryk6IAAFidoooslGwdYVSurjCAW30Tfvu4w8m9azhUoSldfefY+oQ+p621Ug/PI3noznNn4coe4mETooWEJNfdxdfGYleAJ9Th56Vg56RhZGdp73PScPNacQJdf7VFnw2cp1yKwCZPaXHwpaQkFL3Kx8rMw8rIxc6h1BRdykJhanOhKjKpqgPBxjZzhGRSRBVTRJZTTJzkiSskiSqrhFRcwiZrtICbqqENBVsvwGqhD4dk0NJQS2UDGFyvsffEB9fT1l5eWs37KVTdu2s7O8nEgkypQpR3HbXfdw3OlnoweCzJw5M5X3bsuWLdTU1FCYn0fN5vU4ZhLXMpGOgz8rh4zC4u9wxr6evephV1lZySGHHIIQgk8//ZQuXbpw0UUX0blzZ+66664fXPZ3b3Mw5+/Kyso6qL27/hfIz88/qPPsNLmip0mT5ttxXZeysjIWLFjAggULWLx4MatWrSInJ4cuXbocEM+0NN8fKSW1tbXceeedVFVVAd6kdtq0abRp0yZ9XtOkSfM/Q15eHhMmTGDWrFmpZQsWLKB///506dKFYDDIq6++im3bdOnSheeee45AIEB2djaVlZX07dsX13WZPXv211bsbFVcSKe2rVm7ZRsAL898m5HDh3Fcq9a4lolZX4vQdFwzCUKg+vxo/iC6KrGF5k32JYQjkUbxT+Dz+2nT+VDqkw4J26Uu6YlzQU2QZSjoqkKWT8WnqWT5dbL9RsrDBjxvul0FMikl8ViUwuxMMvyeuKcIgeW4WI6kMpJAbwy7jVkOlm1TUVvPltooiqYR0JuPncVXvjVFGuqqguU4hAydoK4hhBd+2pA0SVg2QcObQ3iedV9uIZI0cVwXv64TNy0SloVP1wj6DDQhcFGwbItYPJ46rqTjoiqKF/7qukRNbxsJ26EhYVGfsLBct1HIlNQnbeqSDglHUhjQ8GsqRRlBNMUCy8S1kiiKiuIL4CoqSBcQqLggXexE3POaUlRC/iC2oWMlEihAUUgl26fiuJL+LTPpUpBJSWYQLRGha4c2dMo/ml9MmsDtDz1BdnY2lmVRkp9LdMNyhKZj5JegZeWhBEKoTfv/CllZWUyYMCElWgJ88cUXdOvWjW7dupGfn88zzzxDMpmkc+fOvPTSS6k0RhUVFRx66KFkZmby3nvv0bNnzz325ZYtWzJo0CDmzZuHoijMnDmTPn360KdPn9290qTENZNY0bDXgZvQfUQikdQyVVUZ0LfvbvvaVTATqX+9pVLsHjjrIgEVgSQSbiDg93uh53sgJb5JcKRDOBzB2SV81EX1BEIBQkpUXBRclMZwVonwhHShoUjH6wOAggtSwW70xHPFnv2/hK4hdInzVas1jq+ipk1VOEJlLOEdtZRETCvVtx1XErNspCT1L4ArJRHTIWw6mK5LVdzezZAhXUEVgmyfSosMH4UhP5k+nQyfQcDQ0VW1WVitaGxfNBrFdV18mpYS9YQQ2JblCeG2Ta9DOzOiTw+mXfYr/nDDDTz33HMccsghBINBpON4HqKKirQtFN0gu1VbFN/iPdrou7JXlavc3Fy2bt1KdXU1c+bMYfjw4WiadsBVpTyYxYpAIJD26NjPZGRkHDDVon4ITW+O0qRJ8+386U9/4pVXXiErK4vWrVszadIkbrnlFlq2bElm5u5vf9N8f4qKivZLmP4bb7zBzJkzAe++eNxxxzFx4sT9Ml7ZXzY4kEjbIE2afcOe7mk1NTXk5eUhhEhV8jRNkwkTJjBw4ECCwSChUAiAYDDImWeeyUMPPcRvfvObPe4jaOjkZWXgui6maaIoiud1UleHlJKMYJB4uBzTTBL0+3GlxFZ1MvIK0fx+6hMWqqYzadJkIg0NKKqKT0iIR0jGbIThQ8ajJG1JICtEMpok5NNwjQzqoxGUpEZeUQFYJrGGCKrhw7VtZDxBEm/el0wmSEbDFGcGMKMmLoJMw6C8oQHTBXQfDZEIILA1H/FYlHb9hlAbt6iL1iOlixEIYieTmJaNq/txHRvHssgMBcjSFRzLJDcUwAz5qLFMcjOC5GSEqA+HiZs2Bf48amtrEUIQCoWIRqOAJBQMEYvHqXVdgqEQDdEoiaSFPxggoAh0Af5AgGBmNvmt2rJqWxlCCJKJBJquo+s68VgMKQQxqdDQEKbOdFCMAPFYBF1RyMgIEWkIUx+3CQSDaJZF0NXRXYu6nZXEG+oJKOAAtmoQDIWw6mswXTB0DUW6xOJxNFXFZxgkUFAysvnFUZPRMgMIVUPoPsKRCK1DCgVYRMq2YsWjhIJBEkInmB1k4pGHc/+MGYyfMAFT9RE2Momv/YKQLfErBiIjGydu8vLb7zF01Njd+vJX58G1tbXk5eUBYBgGqqqSSCQYOXIkPXv2xO/3k5GRQUVFBX6/n7PPPpu7776b6667bo99OSMjgw4dOjBv3jwsy8untm3bNqqqqrAScexoFPDSVGnSBStJNBZD1zR8Ph+RaBTN8HH05EnUN4TRtBiZGZmEI2GklGRlZtIQi+O4klBGBrGEibRNgsEgttUo1AZCSClJJBKpCsnxeBzDMNB1nWg0Sk1NDa1ataKuttq7BkMhTyQEb7tRz2tR9fmJxqIMGDqMyoYIkYpqcB2yMkJemK3jEAgECOgaiUScgKGjaRqRWBxV1/H5/ESjUVRFIRgKEm4IIxRBZkYmDeHGY8rKIhqN4jgOmRkZJJJJLMsiFAzi2iZmMok/EMRFEE8k8Pl8FBbk06dPHyLhCLphUFPfQNi08fn91DaEidsuuj9ALBL18iWGMqiuD9OQtNADQcxEgphpo/j8OJaNY1voPj+qkCTiFnkZQUIYuLEIjgZSE1gxGzuh4CoqdQ0NJB0HfyAIVpKgYTBu3Dgs26KmpqbZMWVkZJBMJjEbjykRjRLKzubUU0/l8ssvZ+zYsV5O5M1byGtV2ugco2DZNvWRKC+99NKPKq6xVwW7iRMncskll7Bq1Sq2b9/OH//4R1zXZd26dT9p8uZv42AuOlFVVUUikaBjx477uyk/W3bs2IHf7z9oC384jrOb+3eaNGn2zPjx4xk9ejSlpaWUlJSkxe59gGVZOI0Dxp+K+vp67r//fhKJBOAVmpg2bRpZWT88KfCPYX/Y4EAjbYM0aX468vLyqKmpQUpJMplESonP50NVVYqKds+1NGDAAJ5//nneeeedPToO1EWitCvtxBfrNyOEIBaL8c4nn+IPZVC/czsTDhvGyvUb2LRtB4f170N9JMKyjVsZMGQoGVnZvPTGW/Tp25cVK1fg8/nJKSjk0D79+OzN13A0H536D2H7nI9IOJKiYYezduGnSNui/2FHsmLh5yTD9YyfOIHVK1awbesWegwaxo4dO9i2cT2duvdCKAqrv1jMoV0645c2X3z6CQVFxZT26MXbr7+OFsygZe9BfDHrfVRNo9/ww1nz2SesWbqYzLMuZNnC+SSiYboNPZytq1dQW1FOca8h1O7cTu32zbTu0Rc3maBh82p6NHp6rV30GS1at6Fz127MevdtsnJyOXLUKN5/83X8fj9jxo3nnbdm4rouo8eOY9bHH1Fd38CwI45k4YIF7NxZQfchw6ndvoXasm30HTyU9evWs/jzTzly0lHousHShQto3b49rdu1Z+5HH5CbX8ihffsz592Z+AIhBowcxefvzkRXVQ4fM5ZNc2dhmUnGjhvP+qWLea+uBmX4EJYuWsCmLVs4rFdXdlZWsWrjZgZ264IZrmPR6g10adeKgqCPjxcvo2VeDn26lDJz4Upyc3OJ6wHi4QaycvM4/tipvP/qa8yONDB5xGA+XvgFEdNh4rixLFm9lvK6Bvp0bMuhLQr57wvPU9sQZs6cTxCOgxQCoaoYPj8dOnZk5cqV37kvL1++HPDm+I7j4Pf7UVWVwsLC3dbv0aMH7du357XXXttjX66pqSEzM5P8/PxU6Ox7773Hk08+SXVlBb1L22HbDvMXL6FHl87k5+Xy8dx5tG3dim6dO/HUC//lkI4dKa+pxfD7yQxlcMJxx/Lcf17EtCxOOv4XzHznXeobGpg4eQrzFyxg586djBo1is1btrBmzRqGDxtOLBZj0aJF9Onbh1AoxJxP5lBaWkppaSnvvPsOxUXFDBo8mGeefwHNH+DIseN45b8vghCMnXI0H7/zFvFYjNETJ7Ny0XxmffA+51x8KWvWraNs61b6DhpCPBJmy7rV9OnTF90wWLpoAT26daNly5Z88MGHtGlZQp/evXnzzTfJyclh9OjRvPDCC/j8Po4/9jieffYZErEop5xwPG+98x7VtbUcM2USixYvYev27YwaOZKdO3eyfOVKhgwahNAN5i1YSI+ePRGqzrPPPcvoseNo074jH7z/HsGsbLr07suH776DVHX6H3Ykc955E4CBYyby8XvvEI9G6DVyLJuWLSZaU0Xf4YdTt30rldu30KFHHxLJJNvWrKBjl640ZGaycelCunbpTKeOpcz++EMKi4rp1acP77/7Dv5giIHDhvPJe+8S8vtIJhPYlo3f0Dj5+ON5+913qaqu5uijjuKLpUvZvHkzRx5+GFW1taxYs45BgwYxbtw43nzzTXbs2MG4ceNQVRXLslBVFdd16d69O4sWLfpRgp2Qe9Fdy3EcPvvsM5YuXcrgwYPp2bMnruvy2muvUVhYyLBhw/araCel5MEHH6SkpISpU6ceMALid0VKydatW4nH43Tu3Pmga///AlJKVq9eTTAYPGjDphKJBBdffDF33nnnHh9kadKk+ZKmR+T27dvZunUrLVq0oF27dkQiEXw+H7quHxDPtQ4dOjB+/PiD7p7U9FxLJBKplBr7GsdxeOihh5g2bVpKsPvd737Hbbfdtl8E2f1hgwONH2sD27a59NJLueWWW9LPtTRpvoJlWWzfvp077riDfv36ccwxx7Bz507uuOMOrr/+embPns3GjRu55ZZbdrsHfvbZZ/zjH//gscceY8mSJVxwwQW0bt2af/3rX6kUPbZtc/kF59G3tC033PcIdfX1TJkyhVWrVvHUA38jM1bXzDNKCEE4FiPqKnTo2h01EMK0bBRV5ebb7+Tq31+DLSFmWoSTJuGkRdxyiDUml1cVBdWxCPh9mA40JE0MVSHDp+O6XkhsxHKImA5+VXhJ6xWBoQgKMnxoikKe38DQtcacdAmilkOD6RDUFHRFkOnTMRTJ4/ffy/HnXexVu5WAEDQkLWKWi+VKahKOlyNOgF8VZBkqRUGdkKGR6dO8QLtdjr1xVTRV7GYTKSW2I0k4LuGERU3Cy8ElgCxDQ1MEG9espHrLRkaMn+yFFkqZCjFsihk0NJWgrhI0NHRV8VJS4VXQzTB0Mnw62T4dxYpj1VXjxCLY0QbcRAwnHsaur8VJxpG2hVVXRVO8st1Qh9Po9KIFg6ihTFwEMz5ZynnjDyOnsBjV53lPOuF6kC5qKBM9txgtlIXi8yMMH064jmhlGQtXr+Wqvz9OVV09oVAIVVUJBoPU1NTQokULxowZw7nnnsuQIUNS/bEp993f//53WrduzSmnnEJdXR033ngj119/PYsXL2b+/Pncddddu6UvWrp0KdOnT+f//u//WLt2Leeeey6ZmZk899xzqZd1tm1z9dVXc9xxx/HLX/6SDRs2cOSRR1JbW8s999zDkMGDqd6wGisW9c6dEFimyY7yctq2apXyVFVVldvvf5BrrpyW8mCVUqIYXlXRlPwiBMmkiaapCFXHFQIHpXnf+Eof+qqXYSSewHQltbFEKsxTKILcgFdERVEEwrG55567+fWvL0c3jMa+7G0rZOhezj7LRLguqpAoUuIk4wjp4iQTXgGJr+xbCIHrODixCAKJYyaQrtv4/BYoeuNxA0LTUHwBFE1H8QVQ/UHWbt7MR3Pncc7Z5+IA0aRJ3LIJJ02iSYtw0sJ2IWHZSCDpSKLWl+0Ial5IeVO1V00RXki7pLGmsiBgqGQYGpk+g5BPJ6BrGI2hrt5xCFThJb4USP78179y3hmnU5ift8frs+mc2ULFFSq6IggE/Kxcu44zzzyTdevWEQqFSCQStGzZkh07dpCdnc3EiRM5/fTTGTNmzJ5u0d/KXvWwUxSF0tJStm/fzgsvvEBZWRnjxo2jtLSU3Nzcvbmrny3p/GP7H03T0ucgTZqfCYlEghkzZvDf//6XiooKzjrrLH7729/y0EMPkZubyznnnLO/m/g/wU+Z6qGyspLHH388JdZ16tSJ008/fb8LZel0F2kbpEmzL2hoaOD+++8nHA7z6aefomkap512GqeeeiqPPfYYBQUFXH755Xu8B+bm5tKnTx8AunfvzkknnUQikdhtHGy5kqdeeh2fz0dRURGtWrXi/fffY+fq5QRzQ4250DwUI8CGLdupisZp19GrCm0YPqRjcdzkifg1Balo+A2dnFCASMJslm8qadmsXLmCrMJC/KEsEBC3HOriFutWLCO/VVsiig8JxB3vnqIIyNRVAqaDakWp3lJFm9ateeG55xg8bjKqESDX7+W9ygno6KpXdODIcRPIywikhEwpJTl+HcuVxCyHVpmShO1Qn3SwXJegrnpzNUXBdCWKEGgCErZLzHKwXYnpuAQ0FSEg06fh01Sk9MS8sGl7x5J0iNoSywFbSlwcT3AsaUlxbg5+XUMICBpaKjuYT1MRQG7Qj19TURUFQ1OxGguEZOiaJ3A4JnZ9Bcn6WuxwLU4sjGtbuLEIVn01dqQBJx7FiYZx4nHcZBK3MYcXjefBjUXRLRMUhcNLMhDVZSTiDalz5IlTBm4iBq6LG633ljs2Vn0NQtXoU5jJoxccx1ufLuSDddvpM2wkoydN4dGHH+Lcc8+loqJit/4YjUZ58MEHKS8vp7q6Gr/fz7nnnst5553Hk08+SXZ2Nr/97W/3mK8+KyuL/v37I4SgtLSU008/nR07duy2rpSSGTNm0NDQQGFhId27d+fpp5+moqICRVVRFLVZuq/yqmrmLVxMm5YtUVUVv9+P67ocNWYUwkzgOFZq224ivlvB1K3btmOaFl26dEbVDZTGFb5YsZLWbdpQkJ/fPEfbLv+PxWJsWrWaLl278p+Xnmfc2HG0a9sW6TrorpUqByFx+cWow8h142jWVyING78K28YO1+HYlrcL55ujsVzLK6wAIBQVFAUhG6u+CJCOiRrMRGmq9ooEx0KIAI5tUlhQyJDBQ4glE15hDMcT26UUOBI0VcV0HRJu428FhIzm9x1HCjS8XJRBQ0NtTCRpqJ4VhRBk+Az8ukZA1wkYupcjExdF7lqb1zPqlHFjyM7cJfVV4zmWCKTwCm4kHbdxbQdHEYhYlEM7tuett97i9ddf51//+hc5OTlce+21XHfddRx99NE/+mXwXhXsysvLOffcc6mtrSUejwMwduxYXn311VRy5wOBr5YhP5goLCxM53jZz7RsvCEfrKiqelAXXkmT5qfknXfe4amnnuLqq6/m448/9vJ4qCqtW7fm+eef5+yzz97vQs/Bzk/5TJZS8tFHHzF//nzAux+ecsopdO3adb+ex4N5XLK3SNsgTZp9Q35+PnfdddduyydPnszkyZO/8beHHHIIl19+OeAV5rnyyiv3uN62snJ8GZn87Zrr+c+LL/LCCy/Qv9uhFKgOdrgWNxFHDYYQhh8pJb26dMKVEqu2EqHpqBlZuK5kxYoV9O7dCxQFhCd85Qb9zepZOq5LQf8+oGiEkybBRJJI0iJpO+TmZBHwGwQ0z7sqkjCJWg6uqmG6krjlYCg6UUeyZu1aBh82koxQiHBjtVlNEc30lM0b1lPa5VBko4eaEAJNFWgqqSIUNTETTRFUJ2xsKUnYLpZroitKqoCG3VipNWY5RG0XTRH4VKXZc0fgCQ+GqpDjF+T4vxyr66pCfsBgx7Z6Eskw3UpyG38j0RUFn6qgShdNOii2iROJeNtWFPyN21CQOMkEyXAtVn01bjKBVVvpyTnJJHakDqumCmlbuLYNjoMVDjc/0UKgNHkoOQ4IwcbqetrnhBCJGNKysGMxpOuiZ2YhNBWtpgLF8FrhJuNIx0YNZuCaJvmRWk7u1YGTJo6hxeGT8WXm8Nnc7sRisT3OtbKysrjtttt2Wz527FjGjh272/JdadeuHVdddZV3njWNiy++eI/rbdmyhZrqau77+9+Zv2ABzzzzDC1atKB///4k43HQtC+9G4E2LVvwiymTdptbrV6/ge5dOjc7jiZPtdR36dIiJwuhKNjxGCIRb1wuCWCjxCNY9Y2m13RcVcfv964h6dj4hEt+0ODzuZ8wedRIWhTl4RcOdjKCtO3UVeNKlzUbNtKta9fGqs17QFEQqopQVZqkrN1W8QcRqorluF5VYkX1BDlFwUbFRkEKiCQtTNvFRBAxHa96rABdCIhCwnHYsr2MZctXEqwyqI9L4qZMraepXjEM4yvTVdcF2wVd9U5BUFPI9CmoAjJ9Fn5VQVEgoCloikLIUFPXFZhoquJVYm48d01lPaSUuI7N2vUbaNe2rWdjwEXBRpCwXaKmRUPCJGZZOK5X4TnDp1OUESAHhYL8fM4+80yOP/54DMMgMzOTSZMmsWnTJg499NA92/w7sldn7f/3f/9HRkYGDz30EP/4xz8AzyOsV69ePPDAAwdMldiDWfBKJBLYto3f7//2ldPsE+LxOLquYxjGt698ANJU8j1NmjTfzgcffMDRRx/NL37xC1asWIHjOAghyM/Pp7q6+ntvz7ZtVq5cSU1NDSUlJZSWlu42yHNdl23btrFx40YyMjLo1q3b/3Rer/z8/J/suRyNRnniiSdS98CioiLOOeec/f4S46e0wYFK2gZp0hy8lBQXccUlF9KiVRs6typmZJ/u5Ps1asq248tqDJ0N16NlqwjFq3KpqV4lRek4qMEMUDRWrVnjhdU1bleRDqp0UGgcv7oOmpT4FJDCJhQyyPIbNCS8cLrM7l2JJC2ipk3SdnGFg5UIY4dyidsupiMRQsEJ5pDQTLTsXKoTX3oSmY5L1HII6ArScVm1eg3dRoxGUTVUBfRdPGUyDM/LzWqsvJmw3cZtSDRFkO8XaKrSGIrqCQf5Qa+Sra4qGKpCls9A30XQKcn0RDhDU5stV4TAUEDu2MC62gpa+AR2LAKOjUzEcGIRrIYazEQMq74GaSYQuoFQVNSMLISiIh0bu6EGOxrGiYZxkwmceBRp27iWhVVfj1nvCXSq3wDXxUlYaCE/QlVAUTCyslB2mf+4UrKpNupVHpUSs6EBadvY0QR2NI6iayi1tYgmD0XXRbpN5T5dUBSCbdqS3bYDWjKG2VBFQV5eygN+f5CbncW0c8+g18AhHD58GAP79yc3L4/qqmoMXUfVDIys3Gbi264zQicRx7Ut1mzYhFTUZuLenlBVhZTbnRCN1wd0bN+hcZGC4g+SME0qyito364dCIGLQGgGxa3boWVkUVxcgisEDTZgZGBrngAWNy00AUs3bedIPYSi6Smvy2ZoQE4GEdMmbrvs5goIxJMOcduhNmZiSYXaRIKEHQPAciSWI4mZEsuVOFKStCHaKMQZKmT5BZoisFxJ7fadrPliI8WhXjiuIGFCwvLMtSe5yLIhnhQkLYGuSnQN/IZNwAe5Icj0gV8HQxVk+71QYEMRtAjplGSY5Ad9mI6DoaoEDC88VjR6/gpUNAXWrFvPYSNGYDVWrnYRhBNJEpaN7bqEk0kcV1KfsLBdSdy2AYlP16GuHlXaCF+QjIwMhBDk5eWxfv36rz3335W9OkJdv349gwYNonXr1ui6nprY+P1+YrHY3tzVj6Kp4svBSH19PfF4PFUNJ81PT2VlJcFg8KCtEOm6Ls6ebtRp0qTZjaaXTLu+bJJSUl9f/71FHtd1efHFF3n22Wc55JBDWLlyJb/+9a8ZNWpUavtSeh4GN954I506dWL79u307duXX/3qV/+zHkg1NTWYpkmrVq326X6klHz66acp7zqAqVOn0rp163263+/CT2WDA5m0DdKkOTBwXZfy8nJisRiFhYVkZWVhWRY7duwgFAqRn5+/mwNGKBgkNxgAxyYr4CcnFOD1mTPJUiVnjxkGUuI6DnY0gq7puGYCNZCBYvhRgxmo/iBGMIOxY8fhqgagNObt8vJhySZPGNf1hCQzAa5Ez8ohpCoYGUGipoVPU/GpKpqSJGm7SEIY/iBlUS90r8F0yEASRiemqNTUJ3AsE4kggReaGtIVQrqCKiQlPfqzLWKiqt64OaApBDWFoK4Ss2yyfDq6ouC4kny/5oXm6SqG6uXBywv60FWVhvp6Pnz3bY75xfEgBHnBAAqe952uKqjSaTw2BzceBctFmk35yDzhTtoWxaqNmpdJYut6pGPjxMJYdZU4kQasBi+81W6oQ9oWQjdQ/QGUQCjlFedaJna4HjfuecFZ4TBuMoljWtgNX87V3cSXYZOOrqIFfOhZWYjGvHBCN5CWiRCCQe2KG3OHNYZDSpCOi5MwPcFO13ESCZx4EjuaJFGXBAmKJshok4eW6QmKsU0rcR2XcLiBmvoG2rdv/6P7spSSiooKIpEIBQUFZGVl4ThOqoBgYWHhbn05GAjQorCAhh1bEIpCfkaAl195maRlc8cddzSeC5uG+jovJ6EQKAKElCi46IYPzXUZN2E8wbwCFKTnjUhT4OUu40m8600CUijYiublS2wk2ug5WltveuHUejY15Q3NpDRHSuqtIHPXVlMVjhFJ2gjNR9JxUYTnqSZdSWVBF/65pAxN87aveA6YAKhCoApPGI7ZTqNHqCCke/kfXfllJG51OMbSj9+hy2HjsNCah+sCUniHoOJ5yGX5BSHdyw2pKiLlNVesFJCv9SS3lZYySdSS2K4XCv51mBYgBIYmUYVCtk/B0ESjN6vnfSelZxddEWgKJCyHymiCpO0gBPg1bTc5Mstv0LX/IKJCh7jnjacpCsnGQo2qEOQF/diO6+WGbPydoaqEE0msxrBbTJNs10VRFKLRKJWVlV9/MN+RvSrYFRcXs27dumaquOM4LF++fI8XRJo0adKkSXMgM2rUKP70pz8xdOjQ1IunFStW8NhjjzF69Ojv9VzbuXMnjz32GLfeeiu9evXi1VdfZcaMGQwbNqyZB91jjz3GiBEjuPTSS9m+fTtnnHEGxxxzDJ06ddrrx3cg0FSFbV9jWRbPPfccVVVVgOddd+KJJ+537zr46WxwIJO2QZo0+x8pJbNnz+bxxx8nPz8f27a58cYbmTdvHm+99RaqqnLttdfu0XFAOjYCMHLy2FRRzchhgynyq7yyaBVj+3QlmUigKTp5ioKi+9Cy89CCmaiBIEL3YUlBXSyOKwSK1xgkAlNoICWqIsHQEFKiN4ZYIrzQPUdKXFfiSontutiul1vOlZK47eJKiSsh6nrFJSKWS6wxDLZs1VKEotKiSw8UIUg4Xg45vwINEc97bNfgTL+moAgIGRqKIsj26/htT/nwaSohQ8OvaWT6DTJ8On5NxQ3ptDxmMppwUFWFDEWi4OAk4l6ON8AxkzixiBeSapmeIKYoKL7GEEgzSTLcQCwawU3GQUqcWNhLiN/oJCNdB2nbnu0c21NkXAfX/FIBEarmLW/MMweeoKP6DRzzS6cWIQSq30AxdBS/H2WXIluumYRGoSkWT+A6mbhNeQoVgZ4d8sKHM7Mw8vKx6mtxrWpc20HVFRzTRdE9VyqrtobouqW4jsPnFVE+n7+a3/3+GtasWfOj+/L8+fO5//77KSwsJB6Pc+ONN7J8+XJefvllAK6++mpKSkqa/U7VdbJbtcNnGAhNo3zVenr16cuwYcN4+umnmTRpEtnZ2eyorkU1/OiaSlbAjyoUnMaeIrGpj8RISgVN00i4NpbjIBG4QiAlRE0Ly3GJmBaRpBe+Wh33BDpXQszyiizUx13Kww7hpCSWAE0FnwZSgbgJlgPRJNRHIV6xEauujNxuQ1EVyMvA80TTJBUVEUSNSW6GjioE2T6FgOoJcgV+PZX/TfJlsQ6BJ1LHGq8lgDxfJi3HjEI1VK+yeyjEnrzxdqVpO8ouq9XbGpZqkxf80sOwfbbaGL763VAaw2K94hmeCBcxbSxXkrRd/JoXLiuEwHIk1bEmIdpstg2AhoTJtsoaiusjtNQNFLt5O4QQGKqKrigE9N3zHsYbz2fHtq3QNI3PPvuMl156iZNPPvlHO8rs1VHqL37xC84880z++Mc/sm3bNlRV5eGHH+af//wnf/jDH/bmrn4U+6MK3N6iqdJMmv1HMBg8qEOSd02UmiZNmm9m1KhRzJs3j+uuuy4VAvvxxx/TpUsXzjzzzO+1rfLychKJBJ07d0bTNAYOHMgDDzzAjh07KC31Em9LKVm1ahVTpkzBMAwKCwtp3bo1a9eubSbYNYW2N33KysrYsGHD3jvwn5AdO3aQTCb3uXBWVVXFK6+8kvpeWlpKfn7+AWG3JhuoqvqzvT//GBs4jkMkEtlHLUuT5udDMpnk8ccf56KLLqJfv35cd911zJ49mx07djBx4kTmz59PbW3tboJdfSzO0h1VdM0uZPPGTSxfv5GJvzyHO//yV15+90P6dO1CxzbtPG8kI4AazEQLZKBnZGGrOi4K0rGY/9k8Jo8aiaZ6oYS2opN0JdGkmaoGCc0L1CRsi/p4kqhpU58wkUBN3CJhu9QlbaKWFwpruXhCnvXlvwB1YZNIbTUZ7bqT5f/So96vQdnqZfQcNBRVQIahkqmr+DSV7EaBQwCGppATMLwE94ZO0PCKVgRUgSpdhGPi2CZ5AYMHHn4URVE4/+QTcBMxpHQbPZSkl9/NtnASscZQYQvFCCCSMa+CqJmgonwHazdto88h7RGKBkhQNRSfzys0gOeR5yYTKD7/HmMLFX8AxR9Amkm0DBvXNHEtEyceRzqep5/b6HyjGAZqIICi616l2FisMX+ag5NIICUs3rCdbj4I6mpzbytFgOsgLQvVH8DXWHnTjiZwkibSkYQ31xDeWocaNLB69uYfH8znwksupWvXrj9asLNtm8cee4yzzjqLESNGMH36dN59912SySRjxoxhxYoVVFdX7ybY1TeE+fDTz+jVqxeRSITX3niTm2++mWeeeYbrrruOt956iyOOOIK8ggLqGiIkbYe6WCLldRUzbSKJBG/NmkPb3gPQdYO4bTdGxUqk9ESl+oRFfdKhMm5REXVS4aMxE2ImmLYnxNU0CKJRgS8iEA64Gjg6OD5QVPAZElWB7JBEz1RJ7FhHp8IB5Gca5PgVsvwKmZrg3dmrGd1uJBmhgNeXDYOgoRI0dDIMHUURX3pJ7hKX6rgudbEkrvT8XB3XJZafwZwP3mPZF19w8i8vRlG/HL+50ssV+XU0dZFYpIGtG9ZT2LlnU9HaVImMbyOkqSmBsQnL+TKlhq4IdGP3HIhNv9BUga/Rk9Gve550SMnaFcsY3L/fN8qP3zQ+UVUFn65TVlbGH//4R0aMGMHIkSN5//33v/WYvom9Ojru2rUrf/3rX7n//vv5/PPPMU2TLVu28Lvf/Y5JkybtzV39KA7msKKioqL93YSfPW3atNnfTfhRqKq6W6nzNGnS7JlAIMAf//hHjjvuOJYsWYJpmnTo0IERI0Z8b+E+mUyi63rqpVFmZia2be+WMiIWi6U87lRVJSMjg7q6umbrbNiwgX/961/E43EWLVrErFmzUoLXruG1TTQrSf8N6+y67Lussze23fRRVXW3/Jrfddvf5XeWZTXz4Fq4cCEjRoz41uP9vm36IXZr+ldRlH1u759q29/Xbt9kg29rE5B+rqVJsxewLIvKykrat2+Ppmn07t2b5cuXc/LJJ/PPf/6Tjh070rZt291+t3nzZk448UT8fj+O43Ds1KmouUV8umwldZEoW+si1MXidGzThty8bCw9gGU7+E0TqbjeSxsh6XFoF+qqKlFdm2B2LqYER9UxNB+1DWFMx0Y3fJTXNhA1LdANKhuixC2bOlshaVq4roNUDWpiFvVRi6jj3RsMLFyhYroqrm3i0xV0Q8eWnjgRiVv4XBtFEeSE/LhJk+I27bxQV2kjLJvMYAaKtLETFpmZGegCHNshU/ehCoEmHQxXoCOJhWNgm6iu4z17bIuTRo8gGg5Ts3kdhpAkkyYSiU9TSUTDuK7Ep6tYto3jSvym5XkMWhaaa6Mno+QGDaLVFQhFIWmaKFYSkYwTTyRRhYaWmUvcrERYNn5dI560EIBPV0naDlKCX1cxUXEVBV/Ij20msVDQbAtXSpJSoCGxLAuzuhZN8cI+k0kLbC+PYCyWRABtVQhX15NwBZoDpisRChhCUJksJxHaxqFti8HnQ+bkohhRktV1ROrjxKpdwlGFsIzzypr3OOK447ziDsnkj+7LjuNQVlZGx44d0XWdPn36MH/+fC6++GIef/xxWrRokXpZuitlZWWcfvrpBAIBXNdl8ODB+Hw+3n//fSzLYv369WiahtB9CN2gNmHSEE3gCIWIA9FYDFUICtp2pKqunqxgkILsTOLxOJbj4qoaViJOOJyg2hKUNyQob7BRNAPZ5A3p6kSjUB+xicYMHEdgORaOruL6VBRpkuUHw6cTUBIE/YK8bB+OtKjP9dG1ADI0i3y/RmFOAEM61HTrQs/iLEI+HUNRyAoFwbWRVhLdTCClJBEJowqB7vdjORLNMPAFg+i6RCoams9HTX0DhgaTx43l8KFDUHUVYegkk0lc18Xv95PUIJE0vXBox8G2bXTdQAANsThC1cjOCNKhdQuKDK94hZVMIhwHXTcwkwkQAp/PR7JRPPb5/SSTSe/Zr/hJJr1rXTd8OI5D3LHRdAMpJY5tEfL7UFUFyzRRVQ1N09Ckjap482DXMr3qsYaB4ni56Hr16I6QLtFohIxQCGnbOK5LwO+ntraWjZs2cWjXbjjSxbZsb9whBJZpYjTmxSsvL2fatGm0bNmScePGYZrmj84dv1cFO0VRGDFiBH379iUSieC6LoFAgOzs7APKq21/JrL8sZSVlZFMJvdKXH+aH8amTZvw+/27vZE5WHAc56DO45gmzU/JnDlz6NWrF3379qVv376p5eFwmCeffPJ7edn5/X4sy0ol1g+Hw2iaRjAYbLZeMBhMiXiO4xAOh8nNzW22TklJScrN3jAMotEoxcXFP/Qw9ytNg7x9WVgjHA4zZ86c1PcWLVowcODAAyIcFkgNQg9m7+0fy3e1gQQStud9oCoQ0r2lq1ev/imamSbN/zRfFeJV1Qt7a9euHddffz2K0ryy6a64rkt2djbFJSXoho8Va9bREIlRkJ/PG/MWM3f+Agxdp2u37li2RVVVFT27d2fqUVP45NNP6di2LbFwPfc+8A9atWjBEUcewYuvv0koI4ux4yfw6quvYjsOo8dPYObMmeyoaaDdoMNYvfAzInV1dBw8ki1rVlJXvoPcrkOpqyynYtN6Qu17IYQgsnEJWkYeodadqf7iYzIKimnTcwBVqxeh6Abr3v8vG8wooVCArkMPZ9vCTyhbs5z+ffuwfP48zFiUMePHs+KLJYSrKxg7djybN29iy8YNjD7iCGKxKIvmf07fXj3Jz83hrbfeon3rVvTp3o0XX3mV/MwQowcP4LnX3sBvaEwc3I835nyOY9uM79+DDxcuIxpPMLZfNxas3URFfZjRvbuybkcFG8srGX5oKRvLq5i7ZiN2pAGf38+ny1fTsSifjiUFvLNoBUVZGQxqV8Qrny8j5NMZ37MTr8z7AiElY7u05qP1O0jYDqM6tWL+1krq4klGlrZkdUUtm3dWMbRFDpUxkzVVDfTKCeJKyReVDZRmBijwaczZVk+RodE1K8D7O+vxuyqFPpV/bavAJwRHZGUxOxxGapLhWRm8VF7HIjPO+PJ6JBAI+BnerogttVFWl9XSOh6kKgkf27V07zeCfv368cQTT9ChQwds295jP/u+/bmpv2qahuM4tGzZkmuuucbz9tyDPiGEwHVdgsEgbdq0obS0lOXLl7N9+3YyMzN57733uPPOO6lvaKBTp04EgiG2bttGdk4u11x7Le989A4lJcV0b9uCN194hvysLE468URefvZZ4okE4446hg/fnMmOyiraDjycDUuX0lBVScf+Q4hVlhHetonMTn0IhuPUrlpBVnE3EjJAcssilNy2BArakFg3ByvoI6vbYGqXvk/C7yNnxDh2LvoYTVqs+vgtzIZaAqrClKnHsPTzeSyc+wl9undn2+ZN7Ni2hVGHH05DbTULlyyhf7dD0RSFOZ/Pp1vnQ2jVooT3Zn1C23btGDhoMK+8/gbZuXmMGTeeZ599FsPn45hjj+OlF54naSY57vgTef/dd6irrWXC5MksXbKEbdu2MfzwkZTv3MnqVSvpP2AgEsnC+Z/T6dBuaJrG5++/hZuM0670EOZ+9AHZuXn0GTSI2W+/hT8Q4PCx45j9yssgYPzRxzL7rTeJRsKMmXw0iz+fR3VlBf1GHEHZlk2UbdlE174DMJNJ1i3/gq49epKRmcnCT+fStmNHWrVrz7yPPqSkRQsGDxrE+2+/RVZWJocfcSRvvvYaPkOnXfsOvPL8swghOPXUU3n77beoqqri2KlTeeutt3no4YeYOHEi+fkFuK5L1569yAgG+Xzep3Tr1pXS9u34x0MPs23bNq699lqee+45srOzyc7O/lH9WMi9XC5SSonjOLtV+hJCeGr0fgz1kFLy4IMPUlJSwtSpUw+6sBMpJVu3biUej9O5c+eDrv3/C0jpTQqabuIH4zlIJBJcfPHF3HnnnRQWFu7v5qRJc0Azbdo0ioqK+PWvf00gEEAIQSwW4/bbb2fevHm8+eab3/mFVFlZGeeddx433XQTvXv35tVXX+X555/n8ccfx3VdDMNA0zR+97vfUVJSwq9+9Su2bdvG2WefzRNPPLHHN8FNz7UOHTowfvz4g+6eJKVk27ZtJBIJOnXqtE/aL6Xk9ddf56STTiIWi6VyMN1www0HhGD3U9jgQOebbCAl2C6ETVhYBnO3waY6T7TTVWidCSNa2zxzx6Xcdfst6edamjQ/gng8znnnncd1111Ht27duPnmm+natSsnnHDC196bbNtOpY8wDIPuPXqQlZlJ127d+eSTT+jTuzf9enbjnnv/juO4nHjiibz2+uvkFxSyZNECDhs2jLy8PCZPmsTMmW9y6/XXIhwboWooho+t5ZXMW7SYQQMHsWjhQjqWlpLfqg0rymvZETFJ2g5ZPo0sn0ZN3KI+aVMedaiOucRMSSTp5blDShRpEvAbSMfy5qaNoZ6uY7Pzi0/oPmgwgWAQn6aQrUvee+ohfnHexWQHfKiKoDDkQwCFGQF01QvLywkGUIQXHqs7JkKAk0xgRhqLAzg2Zm2lF2oaD+MkYl4OONvCScQ9kdS1cS3TK8ag6yAU77tj4SQTuJaJkJIVOypZV1HHsSP6ex5ZUoIAgcB1bNxkDJlM4jpOah+u4wlfTSGHTf93LK9Cr9MY5uokkkjbwUlYSNfFSTggwUq6uJaLdAVmUmImBK4DSUfguPBqpJJxoXz8QvGOt6mYgYSdpsWSnACt/SaDCg3atC1C0VTM2nriVRF2bnSpjCj8V9Yw7JyTueiii1JpEV566SUOO+wwhgwZ8oP6smVZXHDBBVx66aX079+fe+65h9zcXM4999xv7MtHH3007777LoZh0KNHD/Ly8jjkkEPYunUrtm1z3nnncdVVVxGNRjnppJP45JNPyM7OZu7cuQwdOpT8/HwmT57MRx99xO133klGMIhlWYTr6qioruHdDz+kfafObN6+g4KcHEaPGYMQgs3VdSzduJWk5bAzZlOXcKiIOFTHwLSEV5XUFJg2BH0SVVoomoZAkuVz8PkMVCHJNCSVKz6nS9dDaVFcRFHIIKgKnnv4AS667NdkhUIEdJUMnw+tsSiD6tpesZBdBE4ppVfdV9UwXYnluAihEDdNbNf7v92Ym21XT/iv+/9X2bhhPZ/OmcMJp5wGQlAVjeNKSV7Qj9bUBgHhhEnCdr7TPoQQjbkv2e1vuqqiCa+4hKZ6lXh1VUVVlcYiFJK//u2vnH3mmRTm53tFOVwHiUQRClXVVTz59P+Rl5fH4CHD6NipE44UqAoorotiJ9E0lT/PeJSNGzfyu9/9jmAwiBCCd955h86dOzNmzJgf1Jf36kjVcRw+/PBDZs2aRUVFRTPRrrCwkJtvvnlv7i5NmjRp0qTZp1x00UVceOGFaJrG5ZdfTjwe54477uCjjz7i3nvv/V7iSklJCeeddx6333477dq1Y/369VxxxRUAnHLKKZx//vkcddRRnHPOOdx4441s376dnTt3cvzxxx8QlUz3FVLK3V7y7U1s2+bRRx9NeS0WFhZy/PHHo6q75zfZX+xrGxwM7MkGUsLWBvjnEnhrPZRHvQTbuw7/BfD8ckhu/kmbmybN/yR+v5+TTjqJe++9l86dO7NlyxYuuOCCb/2dpmkUFRVyxRXTCARDVFVW8MYbb9BrwEDKd2zjX88u55hjplJVVcW9996LaZoMGJpHqzZt6dN/AL179+ajjz5i/eat/Olv9zNxzGja5WVRmJ9HzY4tfPjWm7QtKaJjxw7c/Kc/4Q+FGDJpKjkdDiUvYJAX0CnJDNApX6E8EmdnJEllzKIybuO4YDpNd40mT27v/u9KL2+Y7Ri06ncEpioICIHlgoNCbkkrGiwXnyEJKQqOBF1VcIF1a9dSV1vDmJEj0QxP+EPVUHCRqora+DLPdSSKELiujTQTXhGISD3Sdb/MWefYuGYCXNerCqsoONEwTuNzSzo2VkMYqhsIxUxi69eg7CHxvbS9Srzut0TSCFXBNS2vmms8iWO6uLaDFXWQrkS6YCU8m1mWwLEEtiuwbEHcVnBccKQnhvilQV1SwRBfvrx0gAQSMOhX7VIUUsjKcLBq61H9BkJAU6kBC0l+ixL+/e9/c8IJJ9CiRYsfHUIIXp889dRTmTFjBt26dWP58uXfSYsQQpCfn89ll11G165dqays5JFHHmHChAksX76cv/3tb/Tv35/S0lL+/Oc/k0gkGDx4MF26dOGQQw5h1KhRzJ07l7Vr1/LbadOYPHky/fr1oyAzhBuPsOjTOeRnZTK0V3f+dPPNPP3oDM4//3wmTpqEYpus2VmNoij4NNvLjxj0hOe4LVJ5F21HELd82C4EDfAZGkKAi6DehJxuQ0gGFOpNFweLkCYIFJRQFTOJSYWgrlEXNwkYGqqiUFNZweoVKzhs5EhUddd+5QJe2Hbc8vLwNRV2MZqNoWSz/8ctG9t1yTD0xiq6XjVkANt1kRICgSDFJS3QG6vWFmYEkICmKM0KTxghFWsvjI8MVUVvFOoCPh1VKGiN1Y0FEqRLh9YtydBVfOLLAiogcG2L/KwsfnPxhUjXRdUNhHCRolEGdy3McC1SUWnRogV///vfmThxIsOGDds7fflHb2EX3n77ba644go6d+5Mp06dmr25zsjI2Ju7+lEYjRVxDkaaKjal2X+0aNHigPDK+KEoinJQtz9Nmp+STp06ceutt3LdddeRkZHBxo0bmT17Nvfddx+9e/f+XoKdEIJjjjmGrl27UldXR1FRER06dADglltuoWXLlgAceuih/OUvf2HLli2EQiE6d+58UOde/TZUVd1n9yQpJQsXLuSTTz5JLRs1ahTdunU7oDzZ9qUNDha+agPbhc+3w91zYUFZ8+nArkggbnkhsmnSpPlxCCGYMmUKpaWl1NTUcNppp32nFDDe/VTw9jvvMu3a6+kfCvHWW28x6923cYG83DzmzpvHmWefw6xZs9m2bSsrlizi6quv5vjjj8cwDPr27cvChQtxXZd3PvyI7Vs207NTR44eP5Y///F69GCIuBHi7F9dzl/uvJ1/3HoTUy+eRtsefQkZXjEIVREUhHyIxiIRrTJdLEdSGbdomjdbrkwlto/bkqAuMR2JIz3vpbjliSAxW9KuWy8QClZjBdqoaRNQJBFVobC4BEVRMB0H1VFQFQVHKAgpvSq4oUzsaBiEAkLgWl5eNmma2LZDTThCDqYn4NkWdl1No4jnFYKwIlEvh1bcREoXJ2HjS9jkRS3qV21oJthZSemJbXbzZP+qJnFdgXS9GgKqAXpARaiKty8XrKiNlZC4Lp73nAtIcBzvnNouRE1PrHSkwJUSE0jgiRr5aoAIoH7NXVoCji2I1zkoSgw9ZAICM+riuBBVBL7sTE4YM4KCgoLv3Fe/DSEEo0ePpnXr1lRUVHDSSSfRokWLb332N6VmeOONN5g6dSqTJ0/mgw8+4LnnngO8eeCGDRs4/vjj6d27NwsWLODzzz/nggsu4KKLLiIYDNKrVy+GDh2KEIK5c+fy7LPPcsghh3Dy8cdx7z13I6XEdlzuuON27rzzTi655BJuvfVWzjjjDDJDIRat34KqCIKaQtx2STqSBvPLwhQJW+LXvRdYCRsaEp74LPD6rpTg0yS24xC3JTFNkFfajZ0xm6DtFcgQgG2a5GQEMYSBkp3H5poG9K/RSSzHTVWKbWYvV1JfV0N+Xj6q2jzipCbu9XmfppLp09EUJVU8Jis7my6HHoraKNDpmlcA4/tUif0+qIqSEtGbRDrVtRE0CXaSgX37EvDvPt62Y1HP47URV1HRM7NRGscsrpS4lkUyUk0kEmHSpEn06NFjr7V9r44O33zzTSZOnMjtt99+UItiBzJfF2+f5qfjm/J3HAwczG1Pk+anoKkIQhODBw/mhhtu4KKLLiIrK4sHH3yQXr16pdb9PteUpml069Ztt+VN2wPvGm3duvX/tFfdrhQVFe2VN5B7wjRNnn322VSF34yMDM4666wDyrsO9q0NDhZ2tYHpwMur4c5PoGKXmiwCyPZDtwLID0JdAhbv9CYradKk2Tuoqvq9J5uaqtBQV8usWR9z2OIlDB8+ggcfeYxwOELStsnOzqKmppZQKMTJp5+BYybZsWM7CxYuYuXKlRx33HF0796dt99+m6uuuoqBAwdSWVnJC88/z3V3/YULzjqDvIwgldEkc+fOo3zbVmzHYWfZDsrKK9DGjEMVgiyfN7XVhCA3oKMKL5SwJMObhFuuS33CxpXgSEnUcgibDhHLJWlLDPXL+7DtuCx6fyZjzvglEUshpKtEEybLli6i/6BBbFq7mpZFhTQkkoSTJj5NI2DoGKqCgkTzh9BVFSceA9fxwlwNPygqQoLfdlGFgROLIBTXiyN1HKyGBi8kNZbANT2XKiklZthmS0OczfEk+UID7MZwXohFBPZXnOoUBTKyJZYpiUcFug+CIYkd391bybYgHm0ayzQf0yRsgel6y10pCeMJduAJFcutBgb58pAIVARil99rjR9XQiIucKscjIiLdCWWCTFTo+OkkSyqWM9FF120119cKYpC165d6dq163f+TWZmJo7jMG/ePGbOnMlvfvMbZsyYQSQSQQhBUVER1dXVhEIh4vE4q1atoqysjJqaGv70pz8xYcIEBg8ezJtvvsm0adPo1asX9fX1vPPOO1z0q19z9dVX0651a6qrq/h49mxWrFiB4zjs2LGDf/7znxx//PEMPKQdyzdvp1h6QnFdwsJ2vT5bEfMKg1THXZK2J3pWxySubDon3r+1Me8/daqDwOWL199g6PFnEQo15omVkh0rFtGqc3filTsI+AxqAlEU5cviXEKAX1UIal+vPdiWRSLh4MaSXwpigCK8QiWGqmA7LtGkRcDQMFSFoK6zY0cZ8z/7lPZnnklmwIdAeJWXf4J5qoJElV++5ZN4Iud/XnmV884+kwKfF/re1BLV8DUT7KTrYIXr0TMyEYqKdD1FvD4S4f333+ePf/wjmZmZe629e/WqyM3NRdd1dF0/oEUB0zS/faUDlOrq6lSOlzT7h507dxIIBGjVqtX+bsoPoqlaT5o0afZMWVkZv/zlL5sJKK7rEo/HEUJw3XXXIYSgZcuWPPzww/uxpf8bRCIRLMvaJ7nH1q5dyzPPPJMKtRwxYgT9+/c/4MYo+9IGBwtNNsjMLeTxJXDfZxBtnIAqQNcCOKUnHNYWcv1e/jrbgXW18OBn8G66SGyaNF+L4zjU1dWRSCTIyckhFAoBXl7jmpoadF0nPz8fRVFwHIeqqip8Pl8qWXosFqO+vh6AUChEVlZWs/to23Yd6D94KEuWfEFdVRUJ08L0GwQzM/G7LrYrcf1Btuws54P338eyLVq1bMlvr7mWBfPmceedd3L77beTk5ODEN6kvbCwkNPPOIN7772XS6/6PYoQxOJxLMuic+fOTD3/UuIZhTx6/eX4zQj1w0bTrkVRyoNHVQRBXSXDp6U8ioJCI9tvIJEkLIeo6RAxbRpMT7gzHYnlShKORAGCmVlefjgJ1XGLkKHSrld/qpMu2e0OwdYUVlc2eHYxNLJ8OkJ4HkXZAR+ZPj96ph89EMLILUS6LsnaKmq3baGwqCXCMrHDtbi2hZ6dhx1pQMvOxW6oQ8+yaPItlq7EyI2QW9NANBwnu2UurmlhR02suIOiOlgWyF20OFUDRQXDDyDRvNR4e0Q34Es/5sYXJ0mBl/7uS+nCbPw0IQB/40ZjuGSgpkQODchCoAAxS6AIBduWJGIytb2A5lLx/lwaMhIsW7aMgQMHfktP9sZjdXV1xONxsrOzU1F8yWSS6upqNE0jPz8/VTClaVlT8a54PE5dXR1SylRf3tUZpmXLltx000288sqrxGIx4vE4WVlZZGZm4rouiqJQWFhIdXU1r776KuXl5ViWxX/+8x9WrFjB73//e7Kyspr15ezsbKZOnUp5eTlXXHEFiUQC0zSJx+O0adOG++67jylTpjB16lS++OILfvvb3zK4c3scKbFth/LqGlzXxXJcWiXMxtyujYJz0iJhS+K2S8LxPEotByKWd92B5y3pz8iiLgH19pedxN++N1WmgOx2xBWor7EJ+XYRXAXoquDrRkxSSqJ1NQSysqmtS3oRpI0YqoK+ywKfJsjzex50miKoT1pI3UdVJE7SdhorGXshunvan66qGNr3e9mqiD0LgC4CVzQfNLiKJCs3D1fzYwndy3EnvQrLYhchWbqulyfPcUjWVnt5/vBetmcVtSQnJ4fZs2czderUvfZyeK8KdscccwzTp0/ns88+o0ePHs0MJIRIJexO88NJ57nZ/+ypqEqaNGn+dwgGg4wdO3a35RMmTGj2PScn5ydq0f824XCYeDy+18Uq27Z5+OGHKSsrA8Dn83HyySeTl5e3V/ezN9hXNjiYCIfDhKNxZpYV8sDnX4p1ugLjS2HaEOiY673xT6FD3xK4cyxc9N/90uw0aQ4KysrKuPrqq1mzZg1XXnklJ598ciona1VVFbFYjFNPPZXRo0cza9YsXn75ZXRd58orr8R1XW677TZ03Zvg5uXlMW3atGaVvYWicPRxv2DKMceSNE00RfL5/PlkZGQQj0bIKWlFZVUVb7/6Muf+5krefe0V8nJzMIVK+559iZuP8PTTT3PyyScjhKChoYFPP/2UkpISzj//fD799FPOOussBg8aRH19Pa0P6cK66gjbwgmGHncaH7zwJB/NfJ0JZ11IXss25Ba1IGBoZPs0HFeSHdAJGTrZfgMBmI5LtUxgqAohQyWjUbgLmw5R2yVomriuQ8/DRqGkcm9J6pP2bmJCk8yVZahkGhq6IggaGvUJk6CukeU3CBo6qqoT8GsYgQzaNYp1rpnAjtQjLQurvtrLVVdfhWsmkbu8XHcb89wZDQ20i8XIMhTcZBK7wcuFZ0dj2LFGzyhX4sS/dDt2bInPc5FDD+mowS9D/tykiWs72HEXnysBiXQkVsxF0yWxsEBXJXH7S5Htq3TWM9FR2DW2TuVLsU4CjoR6U2AoAl2R6IrEcr3tGWaSDg0x7rnxZm67/95UqpCvo6qqit/97nesXLmSX/7yl5x//vkkEgn+8pe/sHXrVhKJBFOnTmXKlCnMnz+fZ555BlVV+c1vfoPf72f69OmpiLVQKMRVV13VLHWXaZocceSRtO/ei+xgANu2+eyzzzBNk2QySefOnbFtm7vvvpsrr7ySefPmsWrVKjIyMhg+fDhdunTh6aef5pJLLkFVVaLRKPPnzycYDHLqqaeyZMkSunfvzmmnncb27dsZPHgwLVu2RAjB9ddfz2WXXcbrr7/On/70J3r06EG37t1RpYuZ9MJLd3UXMW2H2lgCiedhVxf3irAAmK5LwpbEkibRaIx2k8eTV5RFYg9TWInXv2OWs/sf8cLIvT7QHAFk5ubjAjXJr/62+feAJqhN2KhC4NcU0EKUHNqLbQ0xfNEkAV1FEeDXNdSvCG0CTxD/qgDXFKIrxJ5FRZ+medfe10UnClIhuAKYMGGi1xeEaK5+73pUsQhuPNJ8WSKGtC20QJCrfvtb7rr7btq0afOdBOjvwl4V7F566SXmzp3L+PHjadWqVbMbeZs2bXjxxRcPCMHuQGjDD0XXdRxnzxdTmp8Gn8+XGrQcjIiveduQJk0aj+zsbH79618D3sAtGo2SnZ2Noijs3LmThQsXUlxc3CyMNc2BhZSSzz//nBdffDG1bNCgQRx77LHp+98BiiMFb20yeHQjRBrFupAOZ/eGSwZ6/9/TqfNyVUH2/26axzRpfjQtWrTg/vvv569//SuO4yClZNGiRWzfvp2//e1vrF27lttuu43hw4ezatUqjjnmGBYsWEBVVRUbNmxACMGf/vQnVFXFtm38fn+z7TfU1/HK88+yePFiLNvzalq3dg3FLVsz6bjj0datZ96sjwkEQ5S0bMl5F19Khs8gKxhgydw5bNqwns0b1rN06VKuuuoq/v73v7Ni+XKOGD4UG8GYMWNYvnw5x40fQ8Aw+OKLpfiLWxFQFQ4fPZaArvHqg39hzvNPUhcOc+jgw+gx5DD69OmN2eiZFLfsRiHAa7MqBA6Q6dPxaSpBQ8WIW+hJm6raOio3b2DjssVMPvsihKKSYajoqoKhKji2TTweR/UFsCVELAejUdizXEnMsvFpCjHLJm7ZKIogL+AnpnppdQKGRiAYQAlm4c/ORzg2TqFXbMFqqEV+Nb5VStxknDVLlrKxbhPHDhuOE67Hjoexw3W4iTh2uA4AJx7DDu8iKAgQqgbSRfEH0UJBANxEAivc0Jgz38JJJHCTFk7SRNFsrJiDL+Bl+3NciYvEtQRB6UkxSTyhZ7nZQH9fLj6+9CbyN8onUSQp6VCC3wHF8f7mQ6AKbxtFmp/KsnqeeOwxrvvDH74xtVZ+fj5/+9vfePjhh1Nz4lWrVrFixQoefPBBtm/fzjXXXMORRx7JmjVrGDduHJs2baKsrIxIJEIkEuEvf/kLuq5j23YzvQKgoaGBGTMe4tMFC9AUBTMWZfny5WRkZPDLX/6S7du3M2/ePNavX0+HDh1o3a69d+4lzJ8/n3fffZdIJMKGDRu47bbbuOeee1i0aBFjx44lFovRp08fNmzYQGFhIe3atWPhgvkYhoFhGAzs35+/3HM35/3yAm666SZM02TixIkcdvhIjhw5cre0WEFDEvLp1MU8K2caOhHTImpaKSG5zo6xY90XrFi8iHN/9WuCgQCW46b+7jou4UgExfCDoiClJL6LcCeBaKO33p4SdyiNdRc8Uc9LgPh1CT6aKhUnbJea7duoWL2U0cccj+V4+SE9kinv2ICupsZsUdMioGvkBLyHvek4VEe94/brWkqM33WMl7RtzG+IKlMVhYCh49c1pJT858X/cPppp5Ofn9+83UJBqCrScVB0HTf+Zc1l6dg4iQjSshCORf/R/fjtb3/LHXfcQefOnVNeyj+GvSrYHX300fTp02ePf8vIyPjeg2THcaisrCQWi5GZmZly1d4VKSX19fXU1NRgGAbFxcXfKqYczMm78/Pzf/Z5bvY3LVu2PKjzCKaLTqRJ883sKmq/8cYbLFy4kOuvvx7Lsvj1r3/NsmXLMAyD2267jUmTJu3n1npUV1d/73x6BwrBYHCvvwQxTZMnnniC7du3A15OmosvvviAKoC1K/vCBgcTjoTPa/N4Yr1KpHGeYKhwfj+4qD8EtD2LdWnSpPluqKpKVlZWs/vM2rVr6dy5M4FAgNatWxOLxUgkEowfP57HH3+cli1b0qFDBxzHYcmSJdx999307duXoUOHEgwGm20/EomwbMliAqrCJ598Qjwep1+/foweN4GBhx+B7vPTpXtPsrKzyQ8G0BuLHoR0lTFDB1Jw9520LC7m1rvu5m9/+xubNm7kwrNO56Qpk3j/kzm8+uFs8vPzOeWc8wgFg3TvO4CGWIx+R44js7CEYcOGkqlK1q1cgbZjO5++9h+2LV9Mj9v+TE5hHqp0SSZMLNNEKAqObSGEgs/QCQqFgKagCYjF4vhxaFNSRKausGXFUgKKl8A/UwPDUHEdB0eRqJogFqkhOyuHoKpiqAIhXVwU/LqClUwgFNUTORMmiUSCzEAAHZeYquIzDKTrEDR0MgJ+LEMgXAd/URDb8vKUGY2OGpYr0TWdlR/PZ0N5HeNzW5Hdoj2J+jqUcA2YceI1VQgkWrQBkgkEAkPXcIwAiuFH1zVsx0HihRcmaqvR8k006ZCsr0WaJpqZxIrFIMdCbYhiuBK1NknIcXFtQUO9g20L4glB2IE6Kb3iBVKi4OJDoOOFUtoIwtIThhwkURxMXDLQUBH4EAQR+BEUCQVdhPjsg7ksOXoJvXv3/ta+vKuot2HDBkpLSwkGg5SUlOA4DpFIhFGjRvHII4+Ql5dH165d2bFjB+vWrePOO++kb9++DBs2bLe+HIvFWLJ4EW2Ki3jzzTepra2lW7duHHvssZxxxhnk5uYyfPhwNE3DcVysZJKEbRNRIrRu3Zr777+fgoIC/vznP3P77bezceNGTjzxRP7whz+wbNkypk+fTmlpKSeeeCIFBQUUFxby8azZ9OjRg8NHHEbXrt158MEHeeONN6iqquLJJ59k5syZPP/88+QXFGJoGpZlpeZziUSCRCKBYRiYpokBaD6dyoYIrnQJhUJ07d6DzatWkB/woesqjgJSKESSJjHLBNfGjjVgGAZ+f4CgpmFbNoqioKgqOZrl1YuVgkgsAUKg6Tq25YXn6oaObTmEhIuiajQkLFzXRdU1LNtBSPfLuafrkOXX2Va2hc1rV1O1s4yikhKE6yCEQNM0LMsk4Qj8agCzseKxbhhE43Fcy0TXDZKWSSKRRNN0GkyTcCRKVjCQqvgaMAwURcGyLFRVRVEUbMtCNNrNsrxwV9PSqXMdsgN+FKGQSCaIx+MYhkHStFFdC0PXcY0AyfpaVFXH9Ycwow2orgsCTFeA6+JaFpXrV9KtWzd69uzJRx99xPjx43+0drNXZ+29e/emX79+e2XCIKXkgw8+4L777iM7O5twOMy1117bLPeMlJJNmzZxww03oCgKDQ0NTJ06lVNPPfUbB77Wt5S6PpAJh8NYlkVxcfH+bsrPltraWgzDSOVCONhwXTftpZkmzXfk008/JTs7G5/Px3vvvUdZWRn//ve/eeutt3jyyScPGMHupptu4sgjj0xVmj2YyM3N3asvoqSULFu2jOeeey613WHDhjFp0qQDVtDc2zY4mLAceGMd3PFZkHoHQKArcHpPuKgfBH++OmaaNPsUy7JS86WmF1WO49C+ffvU3EpRFLp3786MGTP48MMPeemll3j00Ud55JFHmlX0lK5DpK4Gn99PVmYmhw0byq9//WvmzPuM1577N6PGjWf1ogWsWbWS4SMOQ9d0ln6xmJ49elCYlcG8uXPo2KY1U8ceyePP/5fCgnxGDh7Iw0//G5+QjB95GDPfeYey8nLKdpajGT7atO9Ajk9j3luvUlVVxcjxE9BcCysRI2vYYWxYuYy/XHMFZ154MX5N48lHHuKI8RPpeGhXPnjnHYpatGDgoMHMeedN8vPyGDrySF7974uYUqHH8CNY8PEH1NdUU1m2nRXzP8WJRxk5bgLLFy+ifMd2xk2YwJYNG1i1Zg39hwwjHoux4ovFdO7eg5zcPD6b/TEt27SlS/cefDDzDXLy8hk68kjefe0lAoEAo8ZP4sO33kRXBMcdO5WPPnifSCTCMUcdxdw5cyjbuZMpkyezfOVKlq9azWEjj8ASGut3VDBz3iJatWzBnNmzOLRdGw5p35aX53xESV4Oww7tyPOfziIzGGDquFH854NPEEJw7JgjeOPjOcSTSSYPG8jHa1dQXdfAmO4dWF5lsbmsgsM6tmCn7bJqZ5j+LYuwLZOFNTs5JC+TfE0yO1lJoSo4JFfj8x11qI5KTyPAaqcGXQgGGTksMutxkHRXc1hhhmmQDq31IFvsGFFp01oL4rqSOteknRrCwaXGjdPVH6B/mw7ccsstnHzyyd8r/9dX+3JTLsZWrVpx/fXXp5aVlpby8MMP8+GHHzJz5kwefvhhHnnkkWZjJ1VVUyKYz+dj7NixXHjhhcRiMe677z5GjRpFfX097777Lp06dWLAgAG89tprlBQWcughpcz59DPy83I5+8wzuO+BfwDwq1/9ioceeohEIsGZZ57JSy+9RE1NDYsWLWLAgAH4/AH8Pj8vv/oKq1evZsKECRx++OG88847TJkyhUWLFnHCCSdw7gUX0rlzFx74+9/o3qMHxx37C557/jmCGRlMnHI0Lzz7b4QQHH/iybz50n+JRCKMO+oYFs6dQ3VFOZvWr2f9urVs2riRwcNHUFldzZLFi+nepx+qprLk888oPbQrrVq2ZMGc2eQXl9CpWw8+fvdtMrOzGXL4Ecz874tIVeewCVP4+I1XsCyTI6Ycy4JZHxCuq2PI6HGsW7GMqp076D1kBNUV5Wxdt4ZD+/RDSsnqJYto3+VQfH4/0boalsx6lwFDhvHpRx+Qk5dH30FD+PCtN/EHAhwxbgIzX/byXUw89he8/+brRMNhxh09lUXzPqWqopzho8awddNGtmxYT59BQ0gm4qz8YgndevYiLzeHRZ/OofSQQ+jQoZT3332H4pIS+g0YwNtvvkleTg6jxozh9VdextB1hgwdwlNPPoXjOpx+2um8+eYbVFeUc+JxU1mwaAkbNmxg9IhhVJTvZNHSpfTvcgiGArMXLKZLiwJaF+Ux5/3P6dpvIIMHD+bKK69k69atP3psLuReHCHecMMNLF++nMMOO4z+/fvTunVrSkpK8Pv933uQXFFRwQUXXMCll17KkCFDeP755/n444956KGHUoq6lJIbb7wRRVG44oor2LRpE5dddhlPPfUU7dq1222bUkoefPBBSkpKmDp16gE7cP86pJRs3bqVeDxO586dD7r2/y8gpWT16tUEg0HatGlzUJ6DRCLBxRdfzJ133vmzzpeUJs134bLLLqNjx45cfvnlTJ8+nZ07d3LffffxwQcfMH36dN5555396nHb9Fy75ppr+L//+78DRkD8rkgp2bFjB8lkkg4dOuyVe2osFuNXv/oVTzzxBFJK/H4/zz33HFP+n73zDo+iXP/3PbN903snEHrvvXepIupXELChP45iPceKR+UI9i72eo6KR+WggiAqiEhTBOm9B0JI79t3Z35/LLsmIZAAgWTJe1+Xl2R29p1nnpmd8nmfMnZsvbxmXwwfBAp2Nyzc4+0GW+Qtz4NOhmtaw6P9vamuNXGH2+1m5syZzJ07V9zXBIIz4KtF17hxY66//nqWL1/OsmXLeOmllzh+/Dj33nsv8+fPPy3iyOPxIMveVM7CwkImT57Mk08+SY8ePYBTv7+bb2BM51bsOJHLgGEjadaqFYrGgMOjUGp38Psff/DC3CdxupyYTSZuvGU65tBwfly2lO6dOjHjhil4LKW88cGHOFwe/rdoEd/8+wMS4+NxlxaDx01xaQlFZVYyi8vo0KUbh4+f4LW33ubKq6+le+8+yLKEy6Nw5PgJlnz7NS1at0FWPfQdOJgwo54VP/6AR5Jp2bUXRQ4PblUlWK8l1KjFqNXg8KjYnC6yyhwUOhScbjebFv2XUZOmEWrUE6TTYNBp0cne1D6dVoOqqrg9CqEmAwaNBrvLjVNRcHoUf+dISZJQFW+kmUsBu8ubomfUadFrJLQoGFFJjInGoNOi1Wj8tbJVoMhqw+FyU+JwcWDfPo4eOkC/wUOJCQtFliQizQZkSUKnuNBIEqgeFI+CBMg6vX8ySJYk3LYyVI834klRFH89LlUFj8OKUlbsTfRTvdded1kxHrsNUHEV5qF4XHhKSnCXlWLPK8BR5mTBgUxGJ0Vi0sioLhVHmROPU8VaBiV2GasbShVwouA6pTqYJMm7zVPnWKgkEakHbWw0i4NUJs38f+Tk5NC/f3969ep12rmsqirz5s1Dr9fzt7/9jXXr1vHJJ5/w9ttv+zWETz755LRawx6Pxy/elZWVMWXKFO677z4GDRrkP5fvvPseJl1/Pft27aRt27Z06dIFp9uDzWIhMiKCAwcPMG3aNOx2Ox6Ph7/97W906NCBJUu+Q/UovPjSSxTnZvH6m2+T2CiVWbNm8e2339K6VWsURfHe4yVvEM7BgwdJS0vD7XRyx513MmbMGP7v2usodXhbe9hsVr767+ekpqVRkF/AyNFj0Gm1rFu7hrzcXEaNHecXKmVZ9p83lf/tdrv56P33mDB5Kmi0qKioSHhONVCQZRmjVoNZp0WSTnVrPXX+KapKqd2JW1Fxqypuj4cCqxMF73ntUcGtgsujUGJ3YCstITg88tQ5riHoVPSq/2auek+unBMZZO7bwZArRqM3GPzLfb8X8NbGPOO/VRVUtcK/y29DlmUiTAb0p9LQfeeNLw1Wr9Gg0choTvlKVVU+/PBDrp14FWGRUWgkQFHQqG7vOr7tAfbCPO9v2mnHXVLo/S2VFYLHTUyX/oSltcLj8fDEE08QHh5OcHAwLVu2ZNiwYVVdmqul1lNizWYzq1at4s033yQqKoq0tDQ6depEv3796N27d40fRE+ePElhYSG9e/cmODiYIUOG8N///pfMzEwaN24MeJ2+efNm7r77bsLCwkhLSyMmJoYDBw5UEOx8Fyrf/+12OxaLpTZ3/ZJhsViw2+3+1tKCS4uqqths3qKugXoOORwO0SVWIKghjRs35s8//2THjh38/PPPzJw5E/D+/s9WY+VSU1paypYtW7jiiisCLmXf4/HUWuS7qqr8/vvvfPfdd/57/tChQxkwYEC9vmfWpg8CAVUFmxs+3ALv/gmlp9oOamWVCS3h/j5SjcU6gUBQPVarla+//ppNmzZx5MgRoqOj6dKlC//73/948803OXr0KGPGjDmtNh3An3/+ybJly2jatCkZGRmEhISc1hhAZzTSoVc/ekTF4TGYybG7yLMUYdJpsDrdHC+xkV9QQEpSIp06daK0sIAVPyxjw4YNxMQlkOeCyNBISm0ODh4+THJyCqs2buHmqdejDw7FY7MQExlNgtFEa40ORWdAGxzKvffcTVpaU8ynut4CmDQSjoEDaNqyJSHBwf6C81eMGs2xY8fQGHSUulVKHW5kt4Lb5kYne9NOS51u9h09jhIUjqTRUWpzUOzw4MKDy+1BtjkwGw1oJNC73BgNemSNBp3GK+YZTtXCKl8jrMzpwuH2dvO0OP/KcLF7XOhkCaMGbKjoLTaCDHqC9DoMOi2KquJwufGcssvhUbC73BTbXRS6oSi/lCC9hlyrHY0kERlkPNUhV0Yrewv04/amvxr1OrSShE6nq1CcX/V4QPGAquK2lqKeug+pqHgspSgOG4rDjsdehiE63nvxVlVcRXl4rBbcdgtGzybiurbErJXxWC0oTifOomIc+cVE5NtxWjyUFEOZwyuKAmglb+fdYkU91XVWwuoCTuQTi5PP3v+InoP6V3kuO51OFi5cyPr169HpdCQnJ9OnTx80Gg2vv/46WVlZDB06tMoSGLt27WLhwoU0b96crKwsZFmmefPmFdbRajTExsXTt2dPdDqvVOIusyBpNOj0OvR6PUVFRQQFBdGyZUtKSkp4/fXXWbdunbc5mSSh0Wix2e2sWrWKRo0asWjRIlq2bOkV0VSViMhwYmNjadq0KaqqUpCVyYP33ElSk+ZeUcwnTGm0tOvUmZRGjYiM9NZV08oygwcPIT39KLIsV4hELP9vWZbJzc1Br9MTEhqK3eHw1lbUeFN53S4XBqMRjSSREhXuT8OuipgQHW5FodjuxK1okGUNRXYnHkWi2ObG7lZwqyqlboky1YDF5hVGgzxQLHvQyzJaGYJ1Gq8YqJ7qMOtwY1FkrHY3QXpvh2GtBHK5/ZDO9O9KzU39+y2BUaslSK9FV0WUZkSQqeJyVcUgqagouB02dCgYJQUJ9VRXZe+6mlPbUNxuZEkGjYTnlPiL4kFRvSK9uzgfWZaQZR233XYb9913H+3ataNly5ZV+rYm1Kpg17VrVzp37ozL5aK0tJQff/yRTz/9lNmzZ9OsWTO2bt1a4wdmXy6278UjIiICj8dDaWlphfXKysoICQnx7sypGsJHiQAAiNZJREFUls15eXkV1snMzGTFihU4HA7Wrl3Ljz/+yEMPPXTGbUuSVCE1pbwqe6Z1asqFfs9XKLaqGmQ1Gbuqdara3+rWOd+xa9OmmnAxjqXb7fbn2NeWTZfab45TnYYEAsHZue6661i1ahVTp06lY8eODBkyBI/Hw44dO+jQoUO9EYEURWHz5s1YLBb/PbEhUlZW5u96CN4GIjNnziQ0NLSOLRP4UFU4XATz/oAl+70RJwA6ycOEpg4eH2giRIh1AkGtIssy0dHR3HTTTYD32hgVFcXcuXPZunUrPXr0oHPnzlXe09q0aYOqquTn55OYmMj06dMrpMMCyAYT+vhGlKHlZJGFDXsOsmLJYponxdJt8EjWrf6VqddPZubMmf53mDFjxnDttddidbnJK7Nh0mkZPHwExz/5hPv/djtfffUlo0aOIDo+Aa1O7432kTQopySnoKBgOnfq7C00fypiCEkiNjyc3r17eQU2WSLIYPDWqnK70ev1FGSkk5CahsHqxOnxXoBcioqiqpQ4PXjM4UiyDr1WQ8cBQ1EUhVKrjUK7BdxuImLjOXnkAOu+/YJBw0fSvVcvdKeifHQab6dLvfYvQUAny+RbvR1prZIH32O5qqpkW13ISIQYNNiLLESaXIQYdAQb9CiqiqKolDic2N0eHG4PuvBoklsZOFnm9I8jSRCq13Ky1I72VLF+o07jjywyaGRCjQaMOi3GUwKULMtIgEeW0Wj0aCSQDd7ISln1RuCpYZF4HHbclhJQTnXjPCV0eOxWPJYSVFXl6rgWJMRFowFUtwu3tQRXUT6u/Gwcudk4CgoJzyvGVmjHeapIqaqCrQyCHDIlLq+AAxKKqhKtatm2ZRebQ4MYO3bsaeejJElER0czefJkwKsRhIWF8eSTT7JlyxZ69OhB165dq0ypbdasGWPHjiU3N5f4+HimTp1aZZkpi81OcVkZUeFh5ObmMn/+fPLy8vh//+//sXTpUnr06MGjjz7qj0a95pprmDRpEoqieGvL6fSMGjmCdz78mFmzZvHZZ59RUFBAYkICzrISbIX5uCxl/vNA8rjp0a0bTkmHR1UJMuixu9wEB5kZ0Lt3Bdt855YrJpoDB/bRpWs3bE5vzbjKb3yhoWH+1PaRo8dg0Omw2awoHoWCvByaNG1GqKzy9BP/pEOnzvTs25/QsDD/dUAry5gNerSyjEdRCDMZsTpdONxuooOMFNudRJoViu0urC4PZq2MWaehzKXg9KgU2T0ogCx50MkS4MasldDJErI5jOjWHSmwuzBqZBxur8in00gE6bXoNbK/g2t5NLKETpYx66qWseRTn1e+lpXflwrnE976i6Ay9ooRhAQHIZ+hbYaqqnjsVkD1vl+XE1ZlnQGP4iF/+wZsWccJa96OJi06MHjwYObPn8/gwYOrHLMm1KpgV1RUxNGjR/ntt9/4448/OHnyJB6PhxtvvJGhQ4ee04uNt5Cjxy822Gw2ZFk+rWGEwWDwiw+KomCz2QgqN8sCYDQaSUhIwOVyER4eTlhYWK107Dgfzld48nE2we5icjEFu0CjfKHPuty38/Wt76FLIBBUT1JSEh988AF5eXkkJCT4a41dd911p6UN1TVbt26lpKQk4AS72NhYf+rGheByufj8889Zu3atf9no0aMZPHhwvRFWz0Rt+aC+oqrexhIFNlhzDN7fDPvy/e+AmLQwvZPELR21QqwTCC4CRqPRG/1TidjYWEaMGHHW7wYHB9OzZ8+zb0DWYJV0FNqcbN13kNcfe4CUuBiynCW8v34Nx44d457XX69QY/yPP/4Ag5mExCSiQ8wYdVpWrVqFJEk0adIEjUbL3x95lJeef464+ERvuhzgdLlQFBWPohBiNKBFQaMqeCQJj6RBo4FIXTBlNjs6rQbjqW2ePJTBy88/Q0xsPH+7+x4MYUEU213+aCZFhTBFxhmpRStLhGhh1S9bSIqLwVZaQlyjv0oWJDVuSvdhozmZfZLjxzIwtm6Fw+7GrFfRaWTMsgZjOdHOrChotRo0Gi129ynRCrCpMm5Fxa5KmCUZuwJ6FWS3B0VVcTpduJBwKhI2j0RufgHHjqXToXciTkX1R6zlOVXMiopZJ+FUVYJR0Clg1mmQVQmnoqJRVNzOUwX8NRpkScLhdvtTBA2n3i1VdKh4m1zoTGaU4FBUjzczR9Yb/CmLisuJoihsWr+Fln0GEWQ2g6riLClEsVtxFeXhzDuJ21KCu7QYd1kprsIi3FYLHrsbR5EdW6m3mUWZTUJRvMX7tR7o4gpj1a5dVZ5qOp2O4cOHn7Y8Ojq6yuXlMZvNdO/e/azraGSJYLOJYLOJ3NxcJk+eTG5uLl26dGHKlCmkp6fz5JNPYjab/efDrl27cLvdtGnTBkVRMIdHsPb3P1AUhfj4eFJTU7ntttt4Zu4c2rf0RvR5XE7/NiWNFkWS0UgyGiT0Oh0hqEhUfTMsLCpk9hNPoNPraNW8ObFxsVjsTn+6tQ/9qXNfURS2/LGB8VdNJPPoCZq0bEVUZASx4WE0T4zlxhtvZMfOnWzftYvuvXojSRCk1yFLXnHXoP/rdxtkMvi3FWryRuTa3R6K7Q5cHpUiuxObW6HI7sapqBQ7vOmmTpfLn5niBizFueQf3k9So8bYAZOsQS9LGHQybhVk1Sv+eYU+v6eINBtOE92qw6jTEmTQn+FZUIOqapAVF+t//4O4uHhMZ3i+V09FSGp03v1w2crQGL1dhjV6Pe7SQlSdAXtBLmarV5Dt06cPb7755jnZW5laVX2eeuopPvjgAwYMGMC4cePo1q0brVq1wmAwnPPDcmxsLLIsk5GRQYsWLdixYwchISEkJiZWECmaNWvGvn37GDBgACUlJRw7dowWLVpUGCsyMtL/Az527BhDhw7lyiuvrPcP8JVRVZUTJ05gt9tp2rRpwNl/OaCqKocOHcJkMpGYmBiQx8But/vT+gQCwdmRJIm4uLgKM7CSJNG0adM6tKpqjh49Snp6OklJSXVtyjnhdrtxu92YTKZz/q6qemc5CwsL+eKLL3jyySexWq0AJCcnc9999wVEZ/gL8UF9xve4VuKAz3fCon2wN48Kc9dRJri3p8qYVCsatxuomwlVgUBw/uh0OhLjYijeu5dF779BakIcr776KoWFhaxatYqoqCh/SSMfnTp1olPrFhzeugnpuonYJYWMjAz0ej0hISE88cQTfPjhh+zau492HTv5v6f6iqzBWZ/DY9SK4r/dYSczI4O1a9Zwz5130LfcmOXHVlUVl9vNpo1/8Gvuce4bP6Tqd9kx/VFVpUJneR9VWVXVFLuqgqIqOB1OjMa/tqGqKitXrsRms9G/f38OHz5M8zYtaGpU2CXbmDasO97YoKq3WdnUat9WpDNJQ2dHUTxYFYnwpFRCfJHsf4UQoqJCueNV/t/eumMVx/P96VEUnnr66fOw6MLR6XSkJsZz/Ngx5s6dS15eHvPmzcNkMrFkyRLMZjNt2rSpcMzT0tLo0qULP/30E4MHDyYhIYH9Bw7gcrkICwvjrrvu4rvvvmP9hj/oO2DgBb8/BgUHUVxSzJo1a7j11ltp177dKdeefpZ5PB5279nDvt07aXbv3fTo/Fd2iISELEsMHTqUIUOGoKhquc98SKedT1VtS8V7TB0OB3q9HsmbT4qKypYtW9i75ygjB48kKyuLuLg48nIjWOXKYcaw7hWEyepccz6eq4m/FUXB4nITFBVD2Nlq4SYkl/tDrfBPtdw/pFNRft26deOee+45D6v/olYFu3bt2tG1a1cKCgpYunQpOTk55Ofnk5qaSlxc3DnN+icmJnLNNdcwd+5cunfv7k9J0mq13HLLLUyePJnhw4czbdo0nnnmGWw2GwcOHKB///6kpKRUGKtyGqSv2GSgiS3la/EFov2XA6qq+gtTBuoxCLT6VgKBoGYoisLatWvp06dPXZtyThQWFmKz2c458l1VVQ4fPszHH3/MF198QWZmpr/GqF6vZ8aMGXTq1CkgrtPn64NAYFMmvPQ7/HkSypVvQiNBp3i4ryf0SoaszMvXBwJBQ2Dp0qXce++9mM1mHnnkEX9G04033ljls2dKSgrPPPMMa9eu5f777+fYsWOkpqbSunVrfxpfhw4d+Pnnn/110sH3Ql/9db3ypT85OZkXXniBWbNm4XQ6/bXtyqOqKnv37mPBggXMmzePpKQk1q9by9ChQ5Hl04U5uPBn6pKSMr7++msGDhyIy+WiefPmqIDZZKJtmzb859//ZuLEiRj0OpISE4iMCK9ge13e42RZw1VXXYXRZPrLjvK1xc5zXI2q0qxSbblLyaZNm5gxYwaFhYXcf//9JCQkIMsy06ZNq1KgjYiI4NFHH2XTpk288MIL7Nixg9atWxMbG4vJZEKj0dCuXTvefvttCgsLiYqKuiD7zGYzzz77LPfccw+5ubmAt6lI5ZM+MzOTL7/8ktdffx232833S5cyZcoUNFrtaTXgJEmq8dns/QmefnQdDgdfL1xI9+7dcblctGnTBo0so9No6NunNwsXLGDw4MGEhYSAojB0yJAK0XJ1ey7LjBs3jtDQ0LPbcZbPqvpEkqQL7hJbq2/uN954I0uXLuXdd99l2rRplJaW8tRTT3H11Vdz//33n9NYGo2GW265hZtuuonw8HDuv/9+xo8fj1ar5dprr/UXiOzevTtz584lKiqK0aNH8/DDD9e7NKXapKqLhODSEqhCnUAguPz57bff8Hg81a9Yj/BFyZ0LiqLw22+/MXXqVJ555hkOHTrkF+uQNbQbfC3dJsyk2KXDrVQ56VyvOB8f1Hc8Cqw+Bg+ugN8y/hLrNBLEmGFGV5h3BfRrBFr58vSBQNBQcLlcfPvtt1x55ZU8/PDD9O/f/y8R4AwTxZIkERwczIgRI3jyySe54oorKCgoYNSoUf7PO3bsyIEDBzh58mSNrw/lJ9fLI8syI0eOZPjw4ezdu/eMDdh27tzJc889R3R0NM2bN+e3334jPT3dP3ZJSQklJSVn3da5EBwczDXXXMOBAwd47bXX/F15+/XrR2JiInfccQeNGjVi3bp1PP300xw6dAi3283q1avZtm1bnV43VVVl27Ztl1XTJEVRWLRoET179uTBBx9k3LhxaDSaswb8SJKE0Wikb9++zJ49m6lTp3Ly5EkmTpzoP/9TU1NxuVzs3bv3gs9lSZLo3r07kyZN4tChQzidziq/n56ezlNPPYXT6aRTp07k5uayc+dO//3WZrNRUFBQYd8v5HzS6/VMnDiRsrIyXnnlFX+TzM6dO5OWlsb06dNp2bIlhw8f5qGHHmLv3r0oisLGjRtZv359nT6/qqrKzp07sdvtdWbDmaj1QmgWi8X/X2FhIVlZWdhsNkpLS71FFc9B6NDr9QwdOvS05aNHj/b/W6PR0L59e9q3b1/jcQMhPeZMXO51bgKBlJSUgI5S02g0l7wGokAguDQcPHiQEydO0KhRo7o2pcaczz15z5493H777Wzfvr3Ccklnxtz5/8jr/yz3rAonZiOMawFXtoQWUd7uYfWRQH4uqQqnB77bD0+vhTzrX8vTwuH/2sKY5pAY4hXvJMkrqF5uPhAIGhIHDx7EZDJx1113ERYWdkYx7EyEhoZy0003kZ+fT2RkZAUBKDk5mRdffJG77rqrRuUoTpw4wbZt2yq8L5bnrrvuYubMmbRv357OnTuf9vmgQYMYNWoUvXv3ZufOnXTo0AGNRuMXRfLy8nA6nf5r1rp162jRogUxZ0ujqwadTke/fv3o1KkTHo/nNOHC6XTSrl07ioqKSE9Px+12ExkZicFgOKNYcylQFIU9e/Zgs9lq/d3iXM+h2uLEiROUlJRw//33k5iYiKIo5+Rjo9HItddeS3p6OikpKf5zWVVVOnXqxKuvvopOp6tR47KioiKWL1/ONddcU+W606ZNY+bMmaxYsYJhw4ad9nmbNm2YMmUKWq2WoqIiWrRogcFg8NtUUlLCiRMn/PX/t27dSlhY2GldoM8FrVZLu3btePbZZyv0GvDhcrlITExk4sSJHDp0yJ827HA4sNvtdfaO6o2u3cugQYNqvTyJr2Hl+VKrHnn77bdZuHAhxcXF6PV6evbsyZNPPkmLFi1ISkqqN1FJgSx4OZ1O3G53lW3XBZcG38XEVzgz0BBRDALB+aGqKi6Xi/z8fDZs2MAff/zBU089VW/ubQaDAbfbTWZmZkAJdpGRkec025ydnc0jjzxSTqyT0CW0I6j7jehTe6BLaAvmSOweOF4Cb2+CHw56haJJbSHcWP+aGpyLD+o7Fid8th3e+RMKTk1UayQY1Bj+3gvaxFQtnF5OPhAIGhKyLNO5c2eOHj3K/PnzL2gsp9PJu+++W+G+qtfrOXLkCC+++CIRERHVjuFwOCgqKqrQgKgyJpOJ//znPyxYsKDKz3U6HXv37sXhcLBs2TJWrlx5xuf+kpISzGbzJREabDYbdrudffv2XfRt1ZTs7GyeeuqpWg9msFgsDBgwoFbHrA5ZlmnXrh07duzg22+/vaCxHA4HH3zwQYVz2eFwYLFYeP3116vsTlsZt9tNTk4OW7ZsOeM6kiTx7bffsnLlyio/LywsRJZlnE4n33//PVqt9ow6gsViQafTXZJ3XIfDQVlZGY8++uhF31ZNycnJ4cUXX6yyw/CFYLVaL6h+fK1eWeLj45k+fTpdunShadOm/vBRqNuc5MoEctiur85NeHh4XZvSYMnJycFkMhEcHFzXppwXiqIEXMqcQFBX+AQEq9XK+vXr+eGHH/jpp58oKSnhyiuvrGPr/mLixIlcccUVdOvWjdatW9e1OedEYWEhTqezRs0yXC4Xz7/wIsuWLfMv06f2JOqG+egim/gKq1T4jgocLoLn13vrqf29N7SJrl+i3bn4oL6iqlBohzc2wifbwHVqblQrw+hm8MRAb4OJM/n9cvCBQNAQkWWZOXPmBHRAhKD+cbZ06ouFLMs8+uij4lwW1CoXei7XqmB31VVXAVBcXExGRgahoaFERkbicDjQarUVBDyBIFAREQACweWPqqoUFxdz6NAhli1bxtKlS7HZbBw9epTp06dz1113XVD6S21zyy23MHr06IC8x9rt9r/qz50Ft0fh8/8t4v333/enymhjW9Hs+pfp0rkJHeIk2sVAUqhXstt00puWuSPHm6KpqPDzEUgvhjmDoUciaOpJdYOa+qC+oqreaMa5a2Dlkb/EOpMWpnaAmd2qj2wMdB8IBA0ZX5MIgSDQEeeyoL5Rq4Kdx+Ph+++/55NPPuHw4cPceOONzJw5k88++4z4+HjGjBlTm5s7bwL5RyhqvNQ9ZrM5oI+DaFwiEJydsrIy/vOf/7B8+XK2b99OSkoK11xzDQMHDuTBBx+kY8eONG7cuK7NrEBV4fvlO6PXd6qzUVVV9h3O4OnnXqKs1FvsW9KZ6DnlMf51ay8GpEroKrmgfZy3ft3iffDeZsgq80bbHSiAh1bAs0O93UnrS127QDhOVaGosDsXHl8FW7K8fwOEGbxC3bSOYNbVbKxA9YFAIBAIBALBxaBWBbt169bx+OOPM3XqVIKCgsjLy0Oj0aDX6/n888/rjWAXqLXHgAtuAy24cBITEwP6pUI0nRAIzk52djb/+Mc/SE5OZtasWVxzzTWEhIQgSVJA3D8sFgs7duzA5XLRr1+/ujanWsLCwvwFj8+EywMv/3sRB3ZsBEDSaBlwzR289+AEmsVJZxTdokxwY0fongjPrYe1x7yiXXoxPLoSnhvm/ayuL+k18UF9RFG9EXVz18CRor+Wp4TCw33himbe+nU1IVB9IBAIqsbhcPDNN9+wc+dOBgwYwIgRI/yfFRUV8eabbzJ8+HB69OiB2+3mp59+YsuWLbRo0YLx48ej1+vZu3cvS5cupVevXvTp04ft27czf/58f1H41q1bc+21157zc63H42HFihX88ccfNGrUiKlTpyLLMgcPHmTp0qXY7Xb69etH7969kSSJjRs3snLlSqKjo7nmmmsIDw8nOzubr776ikaNGvk7idYUVVXZt28fy5Ytw2KxcPvtt/vf8RRF4ZdffmHr1q3cfvvtmEwmMjMzWbhwIU6nk7Fjx9KyZUucTidff/01hYWFTJo0iczMTHbv3s21116LxWLh3XffZcSIEbRv354DBw7w+++/M2XKlCoDV1RV5cCBA3z//feUlpZy++23Ex0djd1u5/vvv2fPnj106NCBkSNHotPpOHnyJAsXLsRutzNmzBhat26Ny+Xim2++ITc3l8mTJ5Odnc22bduYNGkSNpuNd955hyFDhtCpUycOHz7MmjVrmDZtWkAE0rhcLhYvXsy2bdvo3r07Y8eO9b8LlpWV8cYbb9C3b1/69++Px+Nh5cqV/PHHH6SlpXHllVdiMpk4fPgw3377LZ07d2bQoEHs27ePDz/8ELPZDEDTpk2ZPHnyOT9nKorCqlWr+O2334iNjeWmm25Cq9Vy9OhRvvvuO6xWK7169fJ3bt66dSvLly8nNDSUa665hpiYGHJzc/nyyy+Ji4tjwoQJ6HQ1nGXDe+4cPnyYJUuWUFxczN/+9jdiY2P9n61fv561a9dy++23ExoaSlZWFgsXLsRisTBq1CjatWuH2+1m0aJFnDx5kkmTJlFUVMSGDRuYMmUKdrud9957j759+9KtWzfS09NZuXIlN9xwwxknq337XlhYyIwZM4iPj8fpdPLjjz+yfft2WrVqxZgxYzAajeTk5PC///2PsrIyRo4cSYcOHfB4PCxevJiMjAyuu+46ysrKWLt2LdOmTcPlcvHee+/Ro0cPevbsyfHjx/npp5+46aabar32nY9a/YV8//33jBgxgnvuucffXUSSJBISEsjOzq43qYR12U3nQsnPzyczM7OuzWjQZGZmkpeXV9dmnDcej6fOOi8JBIFATEwMDz/8MOHh4bz11lv87W9/Y8GCBWRmZtb7386XX37JxIkTufrqq/n73/+O1Wqt/kt1jNlsPmtNUEWF77fn8c3nH4LqzbVMTmnMa4/fSfM481kj5CTJm/baLhaeGeJtfOATkA4XwROrvCmznjouV1OdD+ojbgVWHYXHfvlLrJMlaBUFL43winVaueZiaCD6QCAQnBmPx4PdbqesrIw///zTv9ztdvPFF1+wdOlS9u/fj6qqrF27loULFzJ8+HDWr1/PokWL/IJfly5dWL58OYWFhRw6dAibzcaUKVOYOnUqAwcOPC/BR1VVSktLMRgM/PzzzyiKgsPhYNasWSQmJtK3b1+ef/55jhw5wtGjR3nxxRfp168fJSUlvPHGG7jdbn744QcSExPZvXs3hw4dOmcbSktLCQsLY9WqVZSWlvqXnzhxgvnz57No0SLsdjtut5unn36a8PBwWrVqxZNPPklJSQl79uzh2LFjhISEsHLlStxuNwsWLMDtdpOVlcWXX37JihUrUBSFtWvXcvDgwbMGHJSVlREaGsrq1aspLi5GVVW+//57fv75Z4YPH853333n386zzz5LcHAwbdu2Zc6cORQVFbFv3z4OHz5MZGQky5cvx+PxsGDBAlwuFzk5OXz55Zf8+OOPKIrCb7/9xv79+wMmAEJRFKxWKw6Hgw0bNviXezweFi5cyPfff8+OHTtQVZVNmzbx6aefMmzYMLZt28YXX3zhFzM7dOjA6tWrycnJ4dixYxQUFPjP5aFDh55XQIWiKJSWlhIUFMRPP/3k7zL82GOPERERwYABA3jllVfYu3cvJ0+e5JlnnqF79+4oisLLL7+My+Xi559/JjIykiNHjrB3795ztqGsrIyQkBDWrl1LUVGRf3lubi6ffvop33zzDTabDbfbzQsvvIBOp6Njx47MnTuXvLw8Dh06xL59+4iNjeXHH3/E4/Hw1Vdf4XA4KCgo4Msvv2TZsmUoisLGjRvZtWvXWX/3FouFoKAg1q9fT2FhIaqq8ssvv7B48WKGDx/OqlWrWLp0KW63mxdffBFJkujcuTNPPfUU2dnZHDlyhN27d5OQkMAPP/yAoigsWLAAh8NBYWGh//qlKAqbN29mx44dF1V4rtWRrVYrkZGRFU42VVVxOBz1RqyDwO4S63A4RI2XOsZ3wQ5URJdYgeDshISE8MQTT7By5Urmzp1LdHQ0Tz75JIMGDWLbtm3s3buXgoKCevlbOnLkCD/99BOZmZkcOnSIDRs21DsbK3Py5EmOHj1a5WeqChnFCs++t4DCjL8eIm+46SY6tEytsRgkSZAc6hXtBqT+tXx3Htz9A6xK926rrlx1Nh/UR9RT9QAfWQmZZX8tH9AI3hjlrQ+oPccnzEDzgUAgODsmk4kbb7yRbt26+ZepqsqOHTs4evQoQ4YM8S/76aefuPLKK+nRowfXX389P/zwAwDBwcGsXr0a+KssUGhoKI0aNSIlJYWYmJjzEn00Gg1XX301w4YNq/B9rVZLUlISjRo1wmw2I0kS69evp0uXLvTt25cbb7yRP//8k+LiYiIjI9m4cSOFhYWEhoaesw3dunXj+uuvr/Bdh8PBf/7zHyZMmOCPOPYJO77mUmFhYezYsYPQ0FBOnDjBtm3biImJoXHjxiiKwrFjx9i2bRtjx47l8OHDWK1WNmzYQN++fc/qq86dOzNlyhTCwsIArxj1yy+/cPXVV9O9e3fGjx/PihUryMjIICcnh2uvvZYRI0YQHR3Ntm3b/NFTW7ZsISYmhkaNGiHLMkePHmX79u2MHj2aY8eOYbVa+f333+nTp0/ACHZ6vZ6pU6fSu3dv/zJVVdm/fz/bt2/niiuu8C//+eefueKKK+jRowfTpk1jxYoVuN1uwsLCWLNmDS6Xyx8hGhoaSkpKCikpKcTGxp73uTx+/HhGjRpVIcJLo9GQkJBASkoKwcHByLLMxo0badGiBQMHDmTatGns3buX3NxcIiMj2bx5M9nZ2efV2LJDhw5MnTq1Qhdnl8vFf/7zH0aOHOkf8+TJkxw/fpxJkyYxdOhQkpOT2bx5MyEhIeTk5PDnn38SHR1NUlISRqORQ4cOsWPHDoYPH05mZiZlZWX89ttv1Z7Lbdu2Zdq0aURGRgJ/Ra2OHz+e7t27c/XVV7Ny5UqysrI4cuQI119/PUOGDKFx48Zs2rSJkJAQ8vLy2LRpE9HR0SQkJBAcHMyBAwfYuXMnQ4cOJScnh9LSUtavX3/Rz+VazYvr0aMHn376KWPGjMHpdKKqqj9ktlu3bgHzoxQIBAJBw8V3rwoNDWXUqFEMHTqU7Oxsfv/9d7766iuWLFnC999/z9ixY5kzZ04dW1uRQYMGER0dTV5eHsXFxfz000/079//nNIb6hMON7y1Op9tyz8Ht3eipFXr1ky97ppzHkuSID4YnhgAVhdszPRG7x0pgod/htu7wqhmEBt08ZtR+ITBQHwssrngl6MwZ423LiB4xbmxzb1psPHBgblfAoGgdpEk6bQJo5KSEj755BP+3//7f3z99df+z7Ozs4mLiwMgPDyckpISZFnmlltu4ejRoyQmJvoFrOXLl1NcXAzAkCFDuOqqq875HbOq9XU6HSNGjODFF19Ep9PRpEkTf5ZYXFwckiQRFBSEx+PB4XBwxRVX0Lx5c8LCwvy2X8j2VVVl9erVSJJE7969efvttwFvM0edTkdQUBCSJBEWFkZBQQF9+vTh/vvvx26306RJE7RaLfHx8ezZs4ft27czaNAgFixYQF5eHseOHaNFixbnZI/T6aS0tNQvikZHR1NSUkJhYSEajcZvT3h4OPn5+QwYMIB//OMf2Gw20tLS0Gq1JCcns3v3bnbs2EG/fv34+uuvyc3N5ciRI7Rq1eqcfFaXVHUuW61WPvroI6ZOncqaNWv8y7Ozs+nVqxfgLfVgsVgAmDJlCkeOHCEuLs4v0v7666888MADAPTp04frrrvunKPsqjp2siwzatQo3njjDYxGI/Hx8aSkpLBmzRpiYmKQZdkvGtpsNgYPHkxqaipBQUHn3Kn9TOfyhg0bKCkpYdCgQbz33nv+qFZJkvxlZiIiIsjLyyMxMZEHHniAsrIy0tLS0Ol0NG7cmJ07d3LkyBH69OnD0qVLycnJ4cCBA9x2223nZI/b7aawsNAvikZGRlJWVkZRURGqqhIaGupfnpeXR1xcHA888AClpaV+e9LS0ti+fTtZWVn06tWLn376iezsbPbv388NN9xwTj47V2pVsBs/fjw//vgjt9xyC3a7HVmW2bRpE263m0ceeaQ2N3VBBOqLC0BERMR5zeIIao/4+PiArgEny/JFy7EXCC4HFEXB6XRiMBiQJAmDwUCjRo1o1KgREyZMYO/evaxatcqf/lCfJqOaN29Op06d/GkwK1eu5B//+AfR0dE4nU6OHDmCLMv+h+n6wJna3asqrDmusnD579iP/gF4r18TrrySZs2anpffJQkah3sbTjy+CtYdAwXIscBTa2HRfuiWAP0bQad4CNZ7U2gv5BCrqrduntMDJ0pg3XHYk+dtxJAaBs2jwGyX0Ndu0sN5o6rgUb11A4vsYHGBQQshejhYAF/tgh8Pez8DbxrsqGbw2ACINp//ds90HggEgssHXwT4tm3b2LlzJzk5OQwdOpTg4GDKyrwzAA6Hw3//DQ4Opl27dsBfjZSGDx/OP//5T8AbEVdb9+CCggK+//57nnvuOaKjo3n44Yf5888/CQkJ8dvm71Cu1aLX62ndunWtbBu8Yub7779P3759Wbp0KVlZWfz222+kpKTg8XhwuVxotVpsNhtmsxlZlmnUqFGFMXr37s3y5cuxWCx07tyZ33//ncWLFxMZGXnOne21Wi0GgwGLxYKqqlitVgwGA2azGUVRamRPr169WLFiBXa7nZkzZ7J582YWL15MSEjIOYuc9Y3Vq1dz6NAh9u7dy9atW9FoNGRkZFQ4l51OJzqdDkmSMJlMtG3btsIYAwYM4Omnn0aSJLRaba29n5WWlvL1118zd+5ckpKSeOKJJ1i3bh3BwcHk5uaiqioejwdVVdHpdOh0Olq2bFkr2wZvSuo777xDp06d/Ofy+vXradOmDaqq4nQ60ev12Gw2v/CbnJxcYYzevXvzww8/4PF4mD59Onv37mXJkiWYTCYSExPPyR6NRoPJZKKsrMx/Luv1en/9QN81p7w9lYXL3r17s2jRIiRJYurUqRw9epQlS5b4hemLSa0+rYeGhvL222+zatUq1q9fj8PhoHXr1lx55ZVER0fXm5eaQH4g1Ol0AW3/5YBOpwtowUt0iRUIzs6xY8e45557+Oijj05r9KPX62nfvj3t2rXD6XTWu99SZGQkw4YN45dffsHj8bBlyxZ27txJREQEL7zwAt9//z0ajYZXX32VKVOm1Av7ExISqkzbLbTDm+ttnFj1ASguwNt46cYbb7wgsVGSoEk4vDgcXt0A/9sNLsVbk21rFmzLgk+3Q1IIDEuD3snQKAwijBCkBwmvoKWTa1ajrcgBPxyEpQe849vc3m1JeCP5tDKE6RMZ1kRlrBk6xJ7aTh0cGqfHu//LD8PqY16B0a16bZUkr90Ot1eABK9YN7GVN7IuynRh2z7TeSAQCAITVVUpLCyktLQUi8VCYWGh/97pq7PlE8D69+/PTz/9RNu2bfnhhx/o2bPnGZ+1fWKRbxvnG4jhixaz2+3k5+ejqiplZWVYrVZsNhtlZWUoikKPHj148cUXOX78OJs3b6ZJkyb+tNELwW63k5eXh8PhID8/n/DwcG688UaKioqwWq0oioLH4yE1NRVZlvnjjz+IiIjg5MmTpwk/Pjp27MiLL77IwIEDCQ4OpmPHjjz++ONceeWVfnHiTDgcDr89BQUFJCYm0qlTJ37++WcaN27ML7/8Qrdu3WjUqBF6vd7f5ODYsWO0b9++yjE7dOjAc889R58+fQgJCaFjx448+uijjBo1KqCaDKmqSlFRESUlJf5zuXnz5lxzzTX+4+Qrk9KvXz+WLVtGjx49+OGHH+jatesZG0n4zmVfBN/5nsulpaUUFBT4zymj0YjFYsFisfjrSCqKQteuXVm0aBFHjx5l//79xMXFER0dfSGuAfCfw75zJy4ujqlTp5Kfn4/T6URRFNxuN4mJif5ad8nJyRw8eJBbb721yjHbtWvHnDlz6Nq1K6GhoXTs2JGHHnqIQYMGERISclZ7nE5nhXPZ6XTStWtXfvnlF9q2bcuqVavo0qULiYmJREREsHr1apo0acK+ffuYNm3aGe3517/+RYcOHQgLC6NDhw7cf//99O3b96IHU9WqYHfo0CFSUlIYN24c48aN8y93Op2sWrWKwYMH14uXg0CuP5aXl4fNZqNZs2Z1bUqDJTMzE5PJdM4hw/UF0XRCIDg7brebEydO4PF4qvzcJ3objcZLbFn1SJLE6NGjeeqppygtLcXtdvPQQw9RWlrKvn37/ILISy+9RK9evWja9Pwi1WqT0tJSXC5Xhdl/jwLf7FHZ+McGHOl/FXgeM2YMaWlpF7xNX3rsI30hMRjm74Q8q1eQUgGHx9uU4r3N8N+d3sixYD2YTj01KapXaIsyQasY6BgHTSO8UWgGrTdKze72CnT/3ga/ZXjHLI+Kd3tuRcLu1jB/Fyw7DK2jYXiaN8ov3OiN8FNUr3BmdUKZyxudZ9Z6tyVLf0XFGTRese9sjTiqQlUh3wbv/Qnf7oNsS/XfCdHDiKZesS6mFt67qjoPBAJB4OJ2u3nttdfYvn07LpeL1157jUceecQfleZwOIiPjycxMZFRo0aRnp7Ok08+SXx8PLfcckuVAQrh4eHs3r3bn7nVokUL7rjjjvPq4P7555/z66+/kp+fz9y5c3n88ceZMWMGH374Iaqq0rdvX7p3745Wq2XEiBE899xzGI1G7rrrrlq5/2/bto33338fm83GK6+8wsyZMxk9ejSSJFFaWupPaw0JCeGuu+5i/vz5eDwebr755jNGpyUnJ9OmTRt69eqFRqOhc+fOREREMHDgwGrv9Tt37uSdd97BYrHw2muvMWPGDK677jrefPNNnnzySRo1asRVV12FyWTi7rvv5pNPPsHj8XDDDTecMeIpMTGRdu3a0bNnT7RaLR07diQyMpJBgwbV+bPHuaCqKm+//TabN2/GarXy4osv8thjj3H99dcD3ggujUZDo0aNiI6O5uDBgzz55JNERUVx5513Vik+h4aGcuTIEWbNmgVAamoqd999tz9V9VxYuHAhy5cvp7S0lDlz5vDEE08wc+ZM5s+fj6IodOjQgf79+2M0Grn66qt56aWX0Ov13H333dUKuTVh7969vPHGG5SWljJv3jymT5/OyJEjkSQJh8PB5s2bGTp0KKGhodx99918/PHHuN1uJk2adFpUpo+4uDg6depEjx490Ol0tG/fnqioqBrpSfv37+f111+nuLiYt956i5tuuokrr7ySt99+23+Nue666zAajdx99918+OGHuN1urr32Wn/j1MpER0fTpUsXOnXqhMFgoG3btkRHR18SfUtSa3E6895776Vr165MmjTJrxC73W4++OADFixYwPLly+s0OkxVVd555x3i4+OZMGFCQF0owGv/8ePHsdlstGjRIuDsvxzwtWE3m82kpKQE5DGw2+3cfvvtPP/88+LFSCCogoMHDzJp0iS+//77Gv1G6vI64LuvNWnSxP9wZLVaGTduHCtXrjzj9zQaDQ8//DCzZ8+u09TYqu5rqupNGZ2xyMmWD2di+f0DwBs9+N///pfhw4fXms996ar787112dYfh+3Z3qi4c0HCK661ivZG5rkVSC+GnTne6L3yaCSIC/aKatll4FLUcqP89S+jFlJCveKcwwPFDq+o6PJ4Bccwgzfyz6DxRsZZ3V7xcUSaV0iraXqqS4GNJ+CDLV4fVPdQaNJCvxS4vj30TQG95sKjAS/0+cbtdjNz5kzmzp0r7msCQT3hTK+Yvt+37/PKf1de71zGrA37qhq7JrbV1vYrb6+q7Z/JhrPtU3U2X4g/LoY99QlxLp/f9sW5XDvU6lP6tddeywMPPIDBYODqq6/G5XLx6aef8sEHH/D444/Xmx9mfbHjfNBqtQFdg+9yQK/X15vaT+dLIP8GBIJLQWZmJnPnzj3jzKOqqkRHR3P//fdfYsuqx2g0ctttt7F161YKCgr8y333D5vNhsfj4cMPP2Tq1Kn1rvCz0wPvb4ZDhw5g2/GNf3mfPn1qvROXJHnFsVbR0CIKrm8HuVb4NR1WHIZDhV6RzFPNs5+KN4X3t4wzbAdICoXBjeGKpt6UXEmCEoc3BXXhLhd78iRK3FpAQsWbOru/oOrxVNW7vUJ7xeX78702/Ge7d1uxQaei8xRvxJ/d7RX8PKpXBCy0wbFib6fXYvtfYl2wHtrFQI8kSA7x2pJn9UYV9knxRgEG11HarkAgCAyqu1ZX/rwm1/bavf7XfKyL8dx8tjHPxzfnst6FfvdS2FOfEOfy+Y8pzuULp1ZVh969e/PQQw/xzDPPEBYWxqFDh3jnnXd47rnnGDFiRL350fraggci0dHRosZLHZOUlBTQdQQ1Gk3AC44CwcXGarWyZcuWs06QJCUl1bumE3CqMcOECeTk5PDoo49SVlaGRqNh3LhxdOnShdmzZ+PxeMjNzWXhwoXMmjWrTvfBbDb7r0mK6hXKfjzgpuyPT1CshYBXbLz11lsJDg6+aHbIEoQZvf81i/SKd3vz4EABZJZ600QL7SDjFa6sbkgvghOlXpHRl05bHglviuqYZjClPXSIqyhyJYZAyyjoF2thy0mFzUWRrDzqHdOteP1RfixZOpUCe8pXShWPAw4P7Mv3/nc+pITCPT29jSSCzz3L7Lwpfx4IBAKBQCAQCGpZsJNlmTFjxmC327n33nvR6/U8++yzjBgxol4V6Q/k+l1lZWW4XC5iY2Pr2pQGS1FREXq9nvDw8Lo25bzwdQYSCARnJi0tjc8+++ysxXhlWa53Yp0Po9HIjBkziImJ4YsvvqBZs2Y89NBDqKrK0qVL+f333/F4PPz4449Mnz6d+Pj4OrM1LCzMPxF1pBBe3qBSlHkQ245vQfXmk/br149+/fpdUrtMOm+n2E7xXmHMc0og8x1xXw26Aps39XXzSa+4Z3d7RbkQvbeu3ejmXlHubKmjsRHBDA9XGamBu3vArlzYmu1Nq3Ur3u+GGSAhGKLMYHV5I+OOl3ht0sne1Na9ed5OruczrRdq8Ka63tEd2kR7G2JcSsqfBwKB4PLE9wzqcrnIzc1j6dLlKIoTs7k2a8LW9L5cg/Wk87uennXIWn5sqNFwNdzmuZgm1eKgF+NJqiZj1vzYquewcg1WrM2xzmmzNRzvYtyKa9MttbO5c1rbZrFg0OgYPnYM0THR/iaUl+I94IIFu7y8PJ577rkKD1lOpxPwvjCsWLGClStXEh0dzUMPPVQvXm4CXbCz2WxCsKtDCgsLMZvNASvYKYqCoijVrygQNGBkWcZkMgVUF7PKGAwGJk2axNixYzEajWi1WjweD1dccQUbN270d5HdvXs3cXFxdXZ/9nXyiklM5e1NcDAfbLuW4M47CIDJZGLy5MlERERcctt8LtFIcKZpx2C9t5bcFc28QprT431RMOtqXt/N54PU1FTCTdC3kfe/s6GWe3/wda5NL/Z2pF24x1vzzrdpX+qvLP31b63sFenig6FVFPRK9oqTQbq6SXUt7wOBQBD4+LpmKopCcXExa9b8TnZ2Jjt27mHFinU4nW6Kip3oDGFoNNW/kmoN4Uhy9QEgWn0kklx9eLCqD4Nq1vNotFjModWqP6oEjho8LsgShAYr1YpJkgRhQTW7Focaq280ZNRCsKH6wfQaiWB99bM1elki3FCDYybLhBqrXy/EoEdfg+Aes0GHoQaR2LLqQaNW/67jcdrx2G3Vrqc4HXisZdWu57YUg1J9UIS7rAS1BnqEx1qK6qnBepYyFJezBuNZ8TirKdKrqLjKbN6HjLOgquAodlEDN2Mtk6obDoBSh4yiVn+elqLiqUZkc6FSSg18p6qUKtX7zu1xU6a4mff0M+iMRvqOHE7rdu1ISkigZ7++hIeH+yfya/t5+oIFO0VRKCkpOW1WtF+/fkiSRFmZ9+SuT930xAyuQCAQCM5GfZhcqg0kSSIkJMT/t0ajYezYsbz55pvk5uZSVlbGN998w+DBg+vMRpfLhc3uYPE++P6gisdSgGXjf/zRdWlpaUycOLHelyKQpfNPIXW5XOfcwd4nvPnQSpAWDnd0gxs7eiP/4JTYKHvFQ99/snS6mFd+3LrgfHwgEAjqB+XfrUpLS9m1aw/Hjx9j9ZrfWb/+TxxOJzk5ZXgUHRqtEVk2IklaDCYJjc6MJFV/fdcaYpDl6ut4a43xyJrqyx+phmjQnr0jp1ujRQ6NqvbCqMighlIDYU9FG65We52VUDGFQk1ue2FB1UdEm3QQYa5+MINGItJYvXBm0EjEmqu/4WlliSizvtpnqgiTAZOuBsKe0YBJX/05oFHcaKleSXLbrLhrIMR57FY8pUVnXUdVVVzFedUKdqqq4i7KR3W7qt2uq6QQ1V29mOQq1qFWJ8QBrhIZj/3sflY9Ck6qFx1VRcVio9rgNFWFUo2EqlTXKAIKkfFU8yNSVZUi1GqlOAcKNXkkc6kKklr9b0ORdBhlCTW/FA8lrP3wM1aoCopeS3BCHHqDnp6DBtKzX19SU1Np1aZNhefvC3mvuGDBLiYmhnnz5gHeyDWXy4XRaPSLdZmZmQQHB9dpuk1lArlGitlsFk0n6pjQ0NCAroMoSVK9f/EVCOqSRo0a8cUXXxAWFuZfpqoq+fn5pKeno6oqjRo1IiYmJuCEPUmSaN++PR07dmTFihUA/PjjjxQUFBAVFVVnNh0t0fDmTihzKFi3foU71xtdJ8syN9xwQ53Zdqmoreuy73QM1l/a+nO1gbg3CQSBiaqqbNiwgZUrf2XZD6uw2e1kZRVjsXrQaE3Ish5ZNqHVm2u3FpNAIBBcYiRJQouEVgIjGlS3CsezcaKy8sB/+P7dj9GFhRCelIDJbGbwuNEMGDSIHj16nHeJuAt+MpIkCb1ej16vZ+PGjXz44Yf+WZYnn3ySsWPHcv3117N9+/YL3VStodcH2FNsOSIjI0U6bB0TFxdXJ6lZtYVoOiEQnB23283ixYv59ddf/csyMjKYPn06I0eO5IorrmD69OkcOnQoICO2dTodV199tf/vnJwc/vjjjzrbF11wFAszEsgoBU9pNmW/vYfq9rY/bdq0KePGjasTuy4lUVFR9Wpisy4QPhAIAhNVVdm0aQuLv1tKaWkRJ04UYrEqaLRmNLIeWdYF3OSWQCAQ1ARfCqwOCb0kY5I1uEtKKTieQWFpCUu+XcTWPzdfUDmqWn1rX758ObIsYzAYWL16NevWreP1119n5cqVvPvuu3Tv3r02N3fe2GzV56rXVzIzM7Hb7TRt2rSuTWmwHDlyBJPJRGJiYl2bcl643W5/nUmBQHA6xcXFfPbZZ3z88cf+ZW+99Rbbtm3jrbfeIiEhgX/961+8++67PP/883Vo6fnTs2dP4uPjycrKoqSkhDVr1jB8+PA6EfN/OGrk10wZRVWwbPwPrkzvBJ9Go2Hq1Kk0b978sn/Z89V6asgIHwgEgYkkScyc+TdmzvwbRUVF7Ni5mxMZx1nx81o2b96Ow2ElJ9eCgh6NxnBKwLs0xdoFAoGgtlFVbw09t6riVBVcOg0h8bHojEa69+pJn8GDaNQohTbt2hEWFnbB17pafTIvKiqiefPmAGzcuJG2bdsyZswYzGYzzz77LKqqiovzBaIoiujwWce43W5xDASCyxi73Y4sy8TExABeAW/16tVMmDCBCRMmoNPpmDFjBu+8805A3tckSaJRo0a0a9eOrKwsVFVl3bp1lJSUEBkZecnsUFXYlw8fbpVwuFWcxzZh+f2jCrXrbr755gYREZyfn4/NZqtQ76ShIXwgEAQm5e+BERERDOjfF1VVue66/0NRFPLzC1izZj05OSfZsnUnv67eiNPpobjEiUZjBkmuUQ072WVFqUENOzSlyJ7qJ6ZVSQ+es9f98mi0eOzV26ZqQNXVoNekrOLRVT8xIUvgkmtS6w4cnuqbTkg6Cauz+mcVj0ZCZ6h+f50aGYO9BvUEZQnZVn1mm2IyYNRWny5oN+ox1qA0lEb1oFGrf1fz2G24bZbq7XPYcJeVVLueu6QEVam+0YG7pBTVVYP1LBYUV/W17jwWO0pNatjZnHjs1fw2FBWn012jphM2twdVqf58LvPI1TanUIFSRcZTg6YTFlWptoadEwVbDZpOuFFx1KBRiIKKRXFjQ8EQGYHWYKDn0EG07dSJpPh4evXvR1RUlL/xBNRePexafQqOi4tj//79ZGZm8uuvv3LVVVeh0Whwu93nnbMrEAgEAsGlRFVVbDabP+Ln8OHDFBUV0b17d3Q6b2pPdHR0QHdbjoyMpGfPnqxcuRJFUfjzzz85ceLEJRXs7G74eKvE8RIVxVZI0ff/9HeGNRgM3HnnnQEbySwQCAQNGV+amCzLxMXFcs01E7zF9t0eXC4nmZknWfbDzxQU5LBp0+ZT3aHP/nLrFfWqfwGWJBmk6l/UIbfaZhIqoMrHqx3JYrFSVFpMQkLCX0OWb+Nd7t/ViWs+ZE9N9hbk0urXkWq43dzcXEoNhgo1fKvCDeTUQIyQJMiuwXYlSUKqwd56O51X35Tg+LFjxCfEV18GS1WhBoITqKg1eeZTlRqotqe0MPXs2oiqqhw+VEiTJo2rF7RDoqsV2ADUiJpEstfUJ1ADTRS3x82x9OM0btyY6s7oGm6Ws7eK8aIC4TU5GHjFuOqwWCxoDQY6tW3L0DGjSUhIQK/To9Fe/GjhWhXsrrrqKm677TauueYajEYjw4YNw+PxsHfvXtLS0upNFEIgNwyIjY0N6JfEy4FGjRoFdGFsUcNOIDg7QUFBmEwmvvvuO4YNG8aSJUtwOBx069YNSZJQVZXc3NyAigRSVMi3glaGcKP3gXfEiBG8/PLL2Gw2LBYLP//8M+3atbtk9+o/T8J3+1VcRZkU/zAbx4Ff/J8NHz6cyZMnB/S19lzQ6XQN/t4ufCAQXN5IkoROp0Wn09KsWVPunJlGaWkpzzzzDLNnzw7o6/2uXbtYt24dt912W7153z0fFi5cSGxsLP37969rU84bRVF4/vnnuemmmwK6Lqrb7eaxxx7j4dlPBnTDyZKSEl566SWeeOKJgP6Nb9++nS1btjB9+vRL/huv1bf2Nm3a8PHHH3Po0CHatm1LSkoKiqLQtWtXhg0bVpubuiACuUaK2+3G7XZjMtVEWxZcDJxOJ1qtNmCF30A+/wWCS0FUVBS33XYbc+bM4a233iIjI4NbbrnFX/LB5XLx66+/0r59+3r/YK6qcLgQfjgEq45ClwR4uK/3sw4dOpCcnMyBAwcAWLRoETfddBPh4eEX3a4yJ3y4RaUkL4vCr+/GtuNbfyps48aN+de//uVPSW4IxMbGNvhrs/CBQNCw8EXhAWi12oDOxtJoNMiy7I/CD0RUVUWWZTQaTUALRIqieDt5arUBvR++80in0wX0fmi1Wv/xCPTfeF1NKtaqYCfLMq1ataJVq1b+ZRqNpt6p9IFccL+wsBCbzVZtqLLg4pGTk4PJZCIoKKiuTTkvFEXB7a5JqoBA0DDRarXcfPPNdOzYka1bt5Kamkrfvn0r1KTo378/3bp1q2NLq0cFXlgPPx0GjwpZZXBLJ4gLhuDgYK655hp/jdmtW7eybt06Ro8efVFfOFQVVqfDb3syKVhwJ7Zd3/nFuoSEBJ577jk6dux40bZfH8nPz8fpdJKUlFTXptQZwgcCQcPDYDBw5ZVXBnTkDUBiYmK9e989H7p06YLZbK5rMy4ISZK44oorCA0NrWtTLghZlpk4cWLA/zZMJhPjxo0L+P1ITk6uM+FU5MUFGKKLWt2jKIo4BgLBZYyqqmg0Grp3707Pnj39y2w2G0VFRQCMGzcOs9l8TsJW+TFkWSYiIuK0SF1VVbFYLBQWFvqXBQUFERERcV4imgQMbgw/Hvb+nWP1imXXtPFOqI0ZM4Z///vfnDx5kqKiIr755huGDx9efd2XC8Dmhi+22sj4bi62XYsrNJl47rnnmDBhQkDPwp4Pdrs9oDvY1wbCBwJBw0Ov19OrV6+6NuOCiYqKIioqqq7NuCAkSaJZs2Z1bcYFI0lSQEyoVocsy/Tp06euzbhgLpffeHR0NNHR0XWy7QYp2AWywnsxX6IENcNkMgVsOixUTEEQCASn43Q6WbhwoT+yDiAvL49Zs2bx7bffoigK48eP56mnnjpVYLpmv6f8/HyefvppTp48icvlolevXtx5550YjcYK682fP5833niDrl27IkkSAwcOZMqUKec9s9c9CZqEw6FCb6OH5UdgVHMI1kt07dqVHj16sGjRIgAWL17MQw89RLNmzS7KdUJVYUOGyi+//IJt61d+sS4lJYUPPviAgQMHiuuTQCAQXOa4XC6OHTtGQUEBqampxMbGVvjc4/Gwf/9+/yRZVFQUzZs3r3f3B5vNxtGjR7FarbRs2ZLg4ODT1rFYLBw4cACtVkvz5s3r5TuEoij+BlupqamnlaQoLS1lz549eDzeLgOtW7e+JOUzqkNRFI4dO0ZeXh7JycnExcVVOEc8Hg+HDx+muLiYJk2aEBkZWe/OIfCWvDp48CAWi4WmTZtW8K3b7Wbfvn2UlHi71MbGxtar3gDlsdlspKenY7FYaNGixWm1nqs7XvUBVVUpKSnh+PHjuN1uWrduXeE3qygKhw4dIi8vD4CwsDBatWp1UfWlBinYBbLoFRUVJYoy1zHx8fEBLfrKsiyaTggEZ6GwsJBXX32VefPm+Zd9+OGHLFmyhFmzZpGQkMBLL73Ehx9+yD//+c8ajamqKt9//z2FhYW8/vrrFBYWMnPmTIYMGUKXLl1OW3fQoEE89dRTaLVa/3/ngyRBcij0TPLWslOB9cfheAm0jgaj0cgNN9zA0qVLcbvdFBQU8Nlnn/HYY49dlOtEmRP+u7mM/F/fRLEWAN6Otc8++ywDBgwI6GvrhRAWFhbwaUgXivCBQNBwyMvL49lnn2XPnj1Mnz6dm2++ucLnNpuNf/7zn7Rr146YmBiaNWt20SaSLoTdu3fz+uuvs3v3bt544w1/VL4Ph8PBM888g8fjwWazkZaWxsyZM+tVFLmqqqxYsYIvv/ySJk2akJ6ezpw5cyqIKbt37+aRRx5h/PjxaLVakpOT61ywU1WVP/74g3feeYcWLVpw4MABZs+eTaNGjfwNwn744Qe++eYbUlNTOXHiBHPmzKl39XEVReGrr75i9erVxMfHk5+fz5w5c/z+LSsr46GHHqJnz55ERETQunVrmjRpUu9+CwB79uzhtddeY/fu3cybN69CZJ3veL399tu0bNmS/fv3M3v27FPdY+sXK1euZNGiRezbt4+vvvqKlJQU/2dOp5M5c+aQmJhIcnIyKSkptGzZ8qLac9GejH2pmy6Xi507d/LJJ5/UmzTCQK5hV1RURG5ubl2b0aDJzc31z/gFIoqi+GfIBALB6djtdlRVpVGjRoB3dnzZsmWMHz+e22+/neuuu4577rmHNWvW1Pi+pqoq69atY+DAgURHR9OkSRPat2/P77//ftq6kiSxefNmZs+ezUcffVQhPbb8eFWVSCi/3PefTlYZ1UxFr/GuW+pUWX7or8/79u1L586dAe9s9LfffsuxY8eqHOtC/9uZcyq6bv9Kv83Dhw9n9OjRyLJ8UbZZ3/8Db+S2LzKjru0JRB8IBILAIj4+nrfffptx48adcR2dTsfgwYO56qqrGDJkSL0UKLp06cL7779Phw4dqvx83759pKen88QTT/DII4+wcuXKKu/pdYnT6eSzzz5j5syZzJo1i5SUFFatWnXaeklJSYwZM4bJkyfXi1qjiqLw6aefMm3aNB555BG6dOnC0qVL/Z+7XC7mz5/PXXfdxaOPPkpkZCRr166td/eMkpISvv76ax555BH++c9/oigKW7ZsqbCOwWBg2LBhTJw4kQEDBtTL3wJA586def/9989Yh3j+/PlMnTqVhx9+mK5du7JkyZJ6GYh05ZVX8uabbxIXF1fl5xqNhgEDBjBhwgRGjhx50SebL0qYjdPp5PDhw/z+++98/fXX7N27l44dOzJ16tR6cYLVxxOjpthsNlHjpY4pKysL6HNIVdWAtl8guNh4PB7sdrv/7yNHjpCfn0+vXr0wGAxIkkRSUlK1kz/lxQRVVSkqKiIsLMzfLSs8PLzKCZhevXoRHx9PeHg43333HXfddRfvvfdehdnsrKwsVq1ahcPh4Pfff8dgMHDw4EE0Gg3x8fE4HA5/uL7JZKJzfBKpwQ72F3vTb39Nh/9rbqWs4CQej4cRI0awdetW/yTb/Pnz+dvf/kZoaCiZmZm43W40Gg0JCQnY7Xb/2GazmcTERI4dO+b3R3x8PJIkkZ2djaIoGAwG4uLiKCgs4qN1cGL1x+D2+jckJIQRI0awf/9+OnTogNVqJT8/3z92XFwcJ0+exG63I0kSsbGxyLLsH1uv15OQkEBBQQGlpaUAhIeHEx4ezrFjx/zd4lJSUrBYLBQUFKCqKkFBQURHR5OTk4PNZkOSJGJiYtDpdJw4cQLwviwmJyeTl5dXYeywsDBOnDiB2+1GlmWSkpKw2Wzk5+f7x46JiSErK8tvt2/szMxMVNVbI7Fx48bk5ORQWlpKSUkJJpOJtLQ0srKycLlcZzyWCQkJnDhxAofDAUBcXBwajYaTJ0+iqio6nY6kpCTy8/Mr2B0aGupPx67qWJpMJpKSkiocy7i4uNP8HR8fT1FRkT89x+fv48eP4/F4Kvik/Njx8fH+Y1ne7qysLBRFoaSkhJCQEEJDQ/12h4WFERkZSXp6uv9YJicnV/C32WwmMjISl8t11t+jQCC4tHg8HgoLC0975gwODsZkMiHL8hnfC2VZpnXr1ixbtoyPPvqIjh07cuedd9ZJOqnT6aS4uPg0oScsLAyDwXDWl/Xs7Gyio6MxGAyEhIQQERHByZMnL3ktLFX11tAtKyursFyWZfR6PYWFhf4SH23atGHXrl0V1gsODiY0NJR3332X48eP849//IMePXpcyl04DVVVyc7OJiUlBUmSaNu2LYsXL0ZRFDQaDR6Ph+LiYv9zQ6tWrThw4ECd2lwVVqsVp9NJdHQ0Go3Gb+fgwYMBrzjUunVrFi1aREZGBr179+a2226rlxmDkiSd9XedlZVFcnKy/3gsW7YMRVHqVXaFr3TUmWzyXZtWrVrFggULSEtL4/7778dkMl00m2pFsFNVFbfbTWlpKWvWrGHRokVs3bqVvXv3MnbsWObNm0eHDh3qhVh3OSD8WLeIGnACweWN70Viw4YNDBgwgDVr1mCz2ejcubM/zaKoqKjaTtG+uncOh4N+/fohyzJOp9Mv5NntdiIiIip8R5IkOnbs6J+dbNq0KePHjyc3N7eCYKfVagkJCcFgMGA0GtFoNGi1WjQajf8a5Utp1Wq1mLQwsJHC/h0AEsdK4GChRPKp70ycOJEvvviCQ4cOoSgKn3/+OZMnTyY0NNQ/TlVj+9J6fNv37YNvHd+DsyRJHCjSsubPHTgOr/GvN378eFq3bu3fr/Ip+5XH9j1A+dZRFKXCupXt9K1T/uFLq9X6BTNJkk4bu/K+lf9e+WU+23zf8X3PN7bP71WNXX4d39iSJPlfajUaTQUbz7R93/cqb9+3vCqfVDe2z26fLeXX8fn7bH4r//fZjmV535U/Tr7z5VyPpc9WgUBQf8jPz2fu3Ll+cd/H//3f/zFq1KizftdkMvHII48gSRJFRUXccMMNXHXVVaSlpV1Mk6tk3759vPzyy6cJdn//+9/PGFnno/J1SVXVOrtWrV69mi+++KLCsqCgIO6///4Ky6qysVWrVrz88svIssySJUv4+OOP61ywq0x1vlVVtV4JQ5UpP8Fbfj+Cg4N5/PHHkSSJ3Nxcbr75ZiZMmFAvohzPlcrHJxDv2zqdjnvvvRdJkrBardx0000cPHiQ9u3bX7RtXrBg53A4WL16NT/99BNLly5FkiR69erFrFmzeO211xgzZgwjRoyoVwekrlry1gZhYWHVviQKLi7R0dEBXQNOluV6VTtDIKhvREdHM2XKFGbOnEnjxo3Zs2cPV111lf9m7Ha7WbNmDS1btjzrvS0iIoLHHnvMH3VVVFTEtm3buPrqq7FYLOzZs4c77rijwkuATxD04Xa7/SJGZRvHjBkDeGfw4+LiKtQBMRqNhIWFVfhO/6YmvtoPxQ7Is8C+EhN9OqYiSZCamsqNN97I448/DsDRo0f56aefmDFjBsnJyRXGMRgMp41d1YOjL6UYwKXA6pwITv7xNYrFG0EXHh7OzTffTHJyMna7HYPBUOXYCQkJZx3b54/KEQuV1wkLCztt7Pj4+NPGrlxPJSYm5rSaN+XrmYC3Nm5Nxk5NTa1ybK1W6/dBZX8bjUZCQ0MrLEtMTKx27Kp8UtWxrMnYlX1ZVUfEqnxSeeyzHUufD2pyLCuP7fF4Avq+LBBcjkRHR/Pcc8+dJnT53sPKp7WXF+0B/+SMLMv+yPa6yg5p06YNb7755mnLDQaD3/bKKfq+aOP4+Hjy8vL8HeKLi4urvMZeCoYOHcqAAQMqLPNNgkRHR5ORkUFsbCw7d+6kXbt2gDet1DcZ4/u/wWCoF6V1fNkOR48epWnTpuzYsYNWrVoBXv9rNBoiIyPJysoiNjaWPXv20Lt37zq2+nSCgoIwGo3k5OSQnJzM3r17mTRpkj+Kv/xkpS/CtL6l9fqo6vfgW6bRaEhMTCQ9PZ0WLVqwa9cumjVrVi/fSctno/n2p6prk16vR5bli35tuuCnm5MnTzJlyhRCQkKYMWMG48ePJzU1FYPBwAcffFAvo5Hq44lRU8xms0hnrGOCg4Pr9QxNdZwtzFcgEHjFgDvuuIPmzZvz559/cvPNNzNu3Dj/70ZVVRo3bkz//v3POo5Wq/ULDaqqMn78eGbNmuVvOhEWFkbXrl1xuVzMmjWLXr16MXHiRObNm+cX+VavXs2wYcNOq6Phu69Wfmg72/22dbS3AUVxLijAmmMSU9uD4dSTwDXXXMPnn3/O3r17sdvtfPTRR4wdO/Y0seR8OFmqsvj3Q1h2LPYv69y5M3379vVHfVVn/+WKqqrEx8cLHzRwHwgElxuyLJ8xTcxisfDWW2+xfPlygoKC0Gg0TJkyhVdeeYWmTZsycOBAXnjhBYKDg8nIyKBXr151FlGk0WjO2BAnIyODDz74gK1bt/L+++9TWlrKgAEDuPfee/nb3/5G69atadasGY899hgOh4ORI0eeNrlzKZAkCZ1OV2XQiqqq3HDDDbz99tskJSWRl5fHwIEDycnJ4YEHHuDVV19l48aNrF69Gr1ez+7du7njjjsu+T5URpZlpk2bxhtvvMHatWvJzMxk9uzZfPXVV2RnZ3PXXXcxbdo05s2bR2JiImVlZfTv37/e3V9CQkK49tpreeaZZ4iOjsZoNNKpUyeefvppunfvTufOnXn55ZcJDQ0lPT2dIUOG1LvGGT4yMzN5//332bx5M4qiUFpaik6nY/ny5fzrX/9i6tSpzJs3j99++42MjAxmz55d744HwNatW/nf//7H3r17efnll7n11ltZu3YtRqPRf6xMJhMnT56kefPmNGvW7KLaI6kXKNHm5uZyxx13sH79epo1a8bIkSMZOXIk7du3Z/z48Vx//fXccMMNtWXvBaGqKu+88w7x8fFMmDChXp4gZ0NVVY4fP47NZqNFixYBZ//lgKqq7N+/H7PZTHJyckAeA7vdzu23387zzz9fby/4AsHliKqqHDx4kPXr12M0Ghk0aBBxcXF4PB5WrFhBcnIybdq0YdOmTezevRuHw0Fqaip9+/b1F+Ovasx33nmHJk2aMHLkyGrSQWDWSvh8p/fvcCN8PxmSTgUqud1u5syZw1NPPYXH40GSJG644QYee+wx//Wu/ExvTa9/qgrv/2Hn/odmUbrmdVC8M98fffQR06ZNo7i4GJfLRXR0dEBeUy8UVVWFDy7QB263m5kzZzJ37lxxXxMIAgC3282+ffv8tSdDQkJo0qQJWVlZ/kjrY8eOUVZWhtFopFGjRhiNxjq2+nSsVisHDhzwTzbExsYSHx/P0aNHiYuLIygoCKvVSnp6OlqtltTU1HpZe0xRFDIyMigpKSExMdFfFzQ9PZ3GjRtjtVrJyMjA7XYTHR1NfHx8vZj8V1WVzMxMCgoKSEhIICoqivz8fFwuF/Hx8f79Ki0tJTk52V9HuL7hdrs5duwYNpuNlJQUQkJCOHHiBEFBQQQHB5Oeno7VasVkMtGoUaM6qeVYE6r6PQQFBVFcXExKSgqqqnLy5MkKx6s+Ho+CggKOHTvm/zs1NRWHw4Esy0RFRXH8+HFKSkowGAykpKRc9A73FxxhFx0dzccff0xGRgZLly7lhx9+YP78+SQkJHDw4EGys7OxWq0XfUcEgkuF6EonEAjOB0mSaN68Oc2bN6+wXKPRMHLkSP/f3bt3p3v37hdh+zAsDb7Y6Y2wszhh7XG4rq33c61Wy/Tp05k/fz6HDh1CVVU+/fRTFi9eTOPGjYmNjSUuLo5mzZoxfPhwunXrVqM0xDwrfPXjb5Rt+AgUbxpNnz59GDp0KODtkGaz2S55Ee76hPCB8IFA0JDQarW0bdv2tOXl00Xrol7duWI2m6vsiFnedrPZXKFWa31EluXToul1Op0/cig0NJQ2bdrUhWlnxZcWWz76svw9RKPRnFYuoj6i1WpPO9/Ll7C42BFctcWZfg++qNKqjld9JDIyksjIyDN+Xrl0ysXmggU7SZIIDg6mVatWtGzZkunTp7NlyxZWrFjByZMn+eCDD9i0aRPjxo1jypQp9VJFDSREOmPdo9FoxDEQCAQBSetob0Td8RJvXbnfMmBCy7/SYpOSkpgzZw4PPvggGRkZKIpCYWEhhYWFFcZ57733uOWWW7jrrrv8M6Tl65X4IvAUReXn33ew6bN/otqLAW+9lnvuuafO6vgIBAKBQCAQCASBQK1W6JUkifDwcAYNGkT//v154IEHWLt2LYsWLWLBggVcf/319UKwq48h1TWlfI0XQd2QmppaL87j80Wj0QR04xWBQHD+RBihR6JXsAPYkQ0ny6BxuPdvWZa57rrrCAkJ4R//+EeF1IbyZGRkMHfuXHJycnj55ZexWq2sWbOGPXv2YLVaadmyJS1btuTAgYPMfvpFig9s9n937LhxjBkzxi/ymc3mBt8wQPhA+EAgEAgEAoGgMrXSdOLFF1/k8ccfrxDuqNVqCQ8PZ8yYMQwfPpzc3Nx6I3K43e66NuG8KSsr89cPENQNRUVFVXYEDBTKd7oRCAQNC6MWeibDkgPg8MDRYtifD6lh3pRZX526UaNG0bp1a37//Xe2bt1Keno6OTk55OXlcfToUSwWC4qi8PHHH7Njxw5KS0s5evQopaWlfhEuNDSUsrIyysrK/Nvv3L03/3z00Qr1V8LCwhr8NUn4QPhAIBAIBAKBoDIXLNhZLBZ+/fVXHA5HlZ/72j+Xz8OuCZXbY1dV4Nr3WfkHvJoUwg5kwa60tFTUeKljCgsLMZlMASvYKYpSL9qxCwSCS48kQc8kCDNCjgXcCqxKh+GVSgVpNBqaNm1K06ZNmTJlCuC95xYUFPD555/z5JNPkpeXh8PhYN26dadtx2q1YrVay28ZU6POPPH8m7Rr167Cur5xAqHOzMVC+ED4QCAQCOo7iqJgt9txu93Isoxer0en0yFJEh6PB7vdjsfj8X+m0WiwWCxVjqXT6fxdhN1uNzabDZPJdE6R1qqqYrVaMRqNaDSaWtnHs23L4XCg0WjQarX+LAG3243T6cRkMp1WMsn3ucPhQFEUtFotBoMBWZax2WwYDIaLbrcg8Km3uQcWi4WPP/6YdevWodVqmTBhAldeeeVpqXwrV67ks88+w263o9frmThxIqNHjxYpf4KLhkhJFggEgUxKKLSN8Qp2AOuPQ5EdIkxn/54kSURGRjJjxgwUReHhhx/GbrdXWCc2NhadTkdWVpZ/YkAOjsXUfgIjb3iAAT1PLyLucrnOOOnXUBA+ED4QCASC+ozH4+Hbb7/lvffew+l0otPpaNu2Lc8++yySJPHJJ5/w3//+F/BO+vXt25drr72Wu+++G/AGPBQVFZGamoosywwdOpRHHnkEVVVZtGgRDzzwAG+88QajRo2qcVae1Wpl8uTJPPPMM1U2MalN3G43s2bNYuLEifTt29e/7KuvviI9PZ0hQ4bQq1cv//qqqpKRkcHrr7/O5s3esiAGg4EJEyYwdepU5syZw9ChQxk6dGi9yUIU1E9qRbArLCzkm2++ITQ09IzrBAcHM3bs2BqdkKqqsmrVKn7++WeeeeYZcnNzefzxx+nYseNp3fVatmzJgw8+SFhYGFu2bOFf//oX3bt3P2sx60BWss915kFQ+4SEhNTbdto1QTQuEQgaNrIEQxrDqqOg4hXu/siEEWneCLyzIUkSer2eW2+9FZvNxvz588nOzkan0zF+/HhuuOEGDAYDhw4d4rstuSw5rEcX35bgRh0Z09NEmPH0DYhrkvABCB8IBAJBfebw4cM8+eSTPP744/Tr1w+Hw8GBAweQJIktW7bwyiuvMG/ePNq2bYvNZuPIkSOkpaX5Rbwvv/ySb775hg8//JCgoCB/TXmn08nXX3+NXq9nyZIlDBs2DL1ef9r2y2ff+cp3qKpKYWGhP3uu/OflvwdU+E75dcpn81Uev/wYf/75JxkZGXTq1Mn/mcPhICcnhzFjxvDHH39UEOwKCwu56667aNq0Ke+//z5BQUFkZWWxcuVKnE4nI0aM4N1332XAgAFV7q9A4KNWlJ+srCxeffXVswpJKSkp/iLTNeHHH39k1KhRtGrVirS0NNq2bcuaNWsqCHaSJJGcnOwPN42MjCQ4OLjaB75Ajr6LiIgQEV51TGxsbEDPhMiyHNCitUAguHC6J0J8sLfhhMUFvx6FQal/dYutDrPZzIMPPsh9991HdnY2qqqSlJTkv7a079iZRRoIjvWuHxcqMTzNKxZWJjIyssGn6QsfCB8IBAJBfebEiRMAdO7cmdhY7829UaNGABw5coSQkBA6dOhAVFQUAI0bNwb+avYYEhKCXq8nKiqKkJAQ/7jp6ens37+fOXPmMHfuXPLy8qoMvMnNzeXll19m9+7daDQahg8fzuTJk/2fu1wuvvzyS9auXcucOXMwmUx8/PHHLF++HMAf2ZaTk8Pf//53PvzwQwwGAzfffDOtWrXin//8Jzt27OCVV17h/fffr6Br+KIA+/TpQ3BwsH+52WymS5cu/Prrr4wbN66CvT///DO5ubn8+9//Jjw8HPC+Q7Zt2xZZlmnfvj3FxcXs2rWrgggoEFSmVgS7tLQ03n//fSIjI8+4ji+//UyUF6FUVSUvL4+YmBj/bH5sbKz/QlGZX375hQ8++IDs7GwmT558mh2lpaWkp6fjdrvJyMggODiY3NxcZFkmNDQUl8vlz6/X6XSEh4dTUFDgf3AMDQ1FkiSKi4sBb4ReeHg4ZWVl/vQNs9mMwWCguLgYRVGQJImwsLAaj11SUoKqqmi1WkJDQ7Farf5UI5PJhNlspqCggIKCAtxuN82bN68wtl6vJyQkhOLiYv8sQ+WxNRoNYWFhWCwWv93lx/Ydg8jISBwOR4Wxg4ODKS0txeVyAd6LrkajoaioCPCKQBERERV8YjKZMJlMFBUV+X0SHh6O0+k8ze6SkpIKY8uy7Pe3LMtERkZSWlp6Rn/7jqXb7fYXONfpdISFhVFUVOT3SeWxqzqWJpMJo9F42rF0u92UlpaSm5uL2WymSZMm5Ofn+4+lb+zK/j7TsfTN4ISHh+NyuSrYHRoaWuFY1nTsoKAg8vPz/ccyIiKiwth6vR69Xo/T6azytyQQCC5/JAkahUHHOK9gB7DuOBTYICHk7N/9awzJX6PW98DuQ1VhfQZsz/krYm9QKiSdIQi/8kx2Q0T4QPhAIBAI6jOtWrUiODiYW2+9ldGjR9OjRw86depESEgInTt3pqysjJtuuokRI0bQvXt3OnbsiNlsrnbcn3/+mcaNGzN8+HA+++wzVqxYwbRp0067H3zxxRfk5eXx9NNP+2vX+dZxOp289957/Pzzzzz77LNERkby1ltvsWbNGh577DHcbjezZ88mMTGRHj16UFxczO7du4mNjWX37t0UFBRQWlrKb7/9htlsPi2wwePxsHbtWp599tkKy2VZZuDAgQwcOPC0/frtt9/o0aNHBXHS15gTvO/bMTExbN26lU6dOtXoGAgaJrUi2BkMBtLS0vxq+/lQVFTEG2+8gdPppFOnTng8HhRF8QsPHo/njJFxPXv2pFmzZmzcuJE33niD4cOH+1V98CryixcvxmazsWXLFuLj4zl58iRarRaj0YjVauXkyZOAN3U3NDSUvLw8vxCi0+mQZZmsrCxUVcVgMBASEkJRUZFfsIqJiSEiIoKcnBxcLhdarRaz2YzFYqkwdnh4OLm5uX5xyDd2dnY2Ho8Ho9GI2WymqKiIwsJCAKKjozEYDGRnZ/tFJLfbXWHskJAQgoKCyMvLw2aznWa3oigYjUaCgoIoLi72jx0TE4PBYPCv4xurrKzMHzURGhqK0WgkPz8fi8Xij9AyGAz+7fu6plb2iV6v9/tEo9EQFBSExWLx+zI0NBSTyUReXp7/wqvRaNDr9f6xtVotERERp43t86XT6fQfS5vN5v9eUFCQ/1j6fKLVatFqtWRnZ6MoSgWhs7xPtFpthbFNJhM2m42srCyKior84mL580Sr1aLRaCocS5+/CwoKKhzLnJwcf8FWn0/KnyfBwcHk5+f7i7aXt/tMY0dFRWE0Gqs8lj5/h4SEEB0dLaIYBIIGTpAeBqbCiiPexhPpxbAt2xt1d6GaSZkTPt8BxafKkYUaYFxLONOwvmt0ixYtLmzDAYzwgfCBQCAQ1Gfi4uL45JNP+N///sfKlSt577336NatG2+88QbNmzfnv//9L19//TXfffcd8+bNY8SIETz33HMVItIqY7FYWLJkCVOnTiU0NJSRI0eybNkyrrnmmtPEvqCgIHJzc8nNzaVp06a0bt3a3+Ri3rx52O12Xn31VVJSUrBarXz99ddce+21/gYYbdu2Zfny5QwbNowWLVqwadMmEhMT6du3L+np6WRmZrJu3TpGjRp1mp2+po8RERE19ldZWdlZtRFZlklISCA9Pf20NF6BoDz1phia0WhkwIABeDwe4uPjadGiBQcPHvR3ozly5AhXXXVVld8NCgoiKCiIkJAQ3n33XTIyMioIdk2aNOHhhx9GVVXeffdd4uPjadeunX8212w2+8N3fcuaN29eIacd8Bez9K2TkpLi737rW9aqVasKue8mk6nC2AAtWrQ4bew2bdpU+F5ycjJJSUkVxm7dujUZGRnY7XaMRiNGo/E0u5s1a1bl2OXXqWps3zq+7xkMBn8nWt86aWlpp43t6/ZXE5/4vlfZJ5Ik0bRp0/Mau2XLlhX8FhQU5I+wvFCfVB7bpQmiODSS9LwDUGrGlQmtUpsTpDv7sUxKSvKHdpc/lpV9UtnuqnzSunXrCutUNXblY1n5PPGJkAKBoGEzqDGEGSDf5q1lt3gfDGkC+gvMmN9wAlYe9f5bAoY3gY6xFy4ECgQCgUAgqBt874IPPvgg999/P5s3b+bmm2/m119/5aqrrqJdu3a0a9cOj8fD6tWrmTFjBlOmTKFPnz5nHHPv3r1s376dH374gc2bN5OVlcWvv/7K8ePHadmyZYV1/+///g+At99+21837pZbbsHlcvHnn38yfvx4f9kij8dDdnY2ixcvZvXq1f4xBg0ahEajoVu3bqxatYq4uDh69uyJ0Whk7dq1ZGVlVXiP8uELhDgXUlNT2bt371lLWcmyfF5jCxoWF/zWHhsby+zZsyuEe/pqyvkijwwGQ7UpsSaTyR9OqqoqY8eOZfbs2bRv356cnBzy8vLo1asXLpeL9957j7Zt2zJw4EC++eYbTCYTERER/P7777hcrgpiHVQsMulL4ZFl2W9PVXZVtaxyeGxNvlfTsWvyPVmWiYmJwePxnDF15HztPp99u9jfqy9jqypklsJTa+DXdJkgNQmnqkHZDeNayNzXC6JMf72Mnu85UFt2V1WfrvwyXztygUDQsIk2Q/9G8O0+799bs2F/vreD7PmKa8V2+M82cJ4K4g3Rw9QOYD5LPWVxTRI+AOEDgUAgqM84nU5cLpc/ZTQtLc1fRslut6OqKkajEY1GQ6tWrTCbzf6MpKpQVZVly5bRtm1bv6iXlpZGVlYW33//PS1atKjwnmMwGLjpppu48cYb2bBhA7fffjvjxo3DaDQye/ZsPvjgA1599VXuvfdedDodLVq0YPLkyUycOBFZlvF4PH7xrEePHrz44otER0dz8803YzKZeOedd/yiZOX3q5CQELRaLaWlpTX214gRI/jiiy/Ytm0bnTt3riAkRkdH+7PQunfvLqLrBGflgp+MjEYjsiyzd+9eOnfuDHjTW59++mkWLFiAoihcffXVPPLII/6adNUhSRLdu3fn0UcfZcmSJZjNZl5++WUSEhJwu90VxggJCeHXX3+lrKyMpKQk3nvvPX+k1OWIaDhx6bG44Ll1sOwgKOCNqFO96V7/3QkmLTzcD7TiWisQCAIInQyjmsHyw97rXGapt3Ns62jQ1PB6Vv6WpAI/HoKNmX8tG9sC2lVTLeNsXd0bCsIHwgcCgUBQn9myZQtvvfUWnTp1IiwsjI0bN6IoCr169WLlypUsWLCALl26YDKZWL16NYmJif7MoKooKSnhp59+4h//+Afjx4/3B9eEhITw3//+l+nTpxMa+lfx288//5wTJ06QlJTEtm3baNSoEUFBQQAkJyfzyiuvcPfdd6PVarnzzjuZMWMGzz33HEeOHCE6OpqjR48yePBghgwZQkJCAiaTCZfLRVxcHG63m3379jFq1KgqU3h1Oh3dunVj69at9OzZs0b+6tixI9OnT+fee+9l7NixxMbGcvDgQU6cOMGrr76KzWYjOzubDh06nOOREDQ0LliwKyoqYs6cOTz33HP+ZZ999hn//ve/mTFjBvHx8bzzzjskJyfz97//vcbjajQa+vfvT//+/Sss1+l03HHHHf6/hw8fzvDhw8/J5kAuuF9QUIDNZqvQLVdwcTla5K3zpOBN7+pgzqLAbeJPSzIeFRbvh1u7eGs/BQIej8ffzEIgEDRcJAm6JULzSG90nQosOQDXt4dIU9XfUVVv9FyeFcpc4HR7a+BllMK2LG+0nu3U5SUlFCa18wqDZyMvLw+Hw3FZT7ZVh/CB8IFAIBDUZ9q3b8+UKVPYvn076enptG/fngcffJDU1FQiIiJwu93s2bOHvLw8+vfvz5gxY4iJifF/v02bNkycOBG93htyb7FYuOqqq+jdu3eFrLfhw4dz4sSJ095Vevbsyc8//0x6ejotWrTg73//OzExMUyaNImYmBgSEhJ47bXXWLBgAdnZ2QwfPpy4uDhWrFhBZmYmzZs3p3379oC3nNY999yDJEkEBweTlpbG3XffTa9evarcd0mSuPLKK/n3v//NTTfdhMFgqNZfer2eO+64g969e7NmzRqOHj1Kamoqt912G6Ghofz5559IkiQ6xAqq5YIFO6vVisvlolmzZgDYbDa++eYbxo4dy6xZs/zF8b/44gvuu+8+cUJeIKqqiii7S8y+fG/0iQ8ZBZm/jkGRHQ4VBo5gJxAIBD6izTAszSvYgTcl9r874f91Aa3sFfV8txyHB7ZkwZL93ii6rDIodXgnM6pibHNoV4P0WpvN5m8M1FARPhA+EAgEgvqM2WxmxIgRjBgx4rTPwsLCGD9+POPHjz/j97t370737t39fycmJnLfffedtl5cXBwPPfTQactbt25dZcTezJkz/f9u0qQJDz74oP/vLl260KVLl9O+o9PpmDJliv9vrVbLAw88cEbbJUmib9++zJ8/ny1bttCzZ88aaRp6vZ6ePXueFpXn8Xj4+uuvmTp1KibTGWZIBYJTXLBg5/F4cLlc/pP26NGj5OXlMXnyZEwmE5IkkZqa6u+KWh8IZNFQp9OJ4pSXmCCdN7LOJ9GVeoxYlb8KMsmSd51A4Uz1DwUCQcNkbHP4YhdklIBHhQ+3QNMIGNHUK9blWmDNMVi4x9tJ1uqC6qaNWkXBlPagqSa6TiAQCAQCgaC+YzKZeP7552sUXVcdkiQxc+ZMoqOjxTuZoFpqpYadx+Nh9+7dhIeHs2nTJiwWiz+8U1VVLBZLvVKPa+OHVlfExMSICLtLTNsYSAr1vsyqwGZLIgp/vYU2jfD+FyjIsiwKewsEAj8pYXBHN5i7xivG5dtg9q/eenayBLtyYU+eN8KuPFr5VBQeXmHOoIHUcOieAONbeq+bNSE0NBSz2VzLexVYCB8IHwgEAoGg/iJJErGx1RTlrSGyLJOcnFwrYwkufy74rT0qKoqJEydy22230bp1azZv3szAgQPp1KkT4I3AW79+Pc2aNas3CvLZOtbUdwoLC3E6naI48yUkPhimd4IX1ntrM7U05VLmMXDIEUWkEWZ0haCzdECsbyiKgsfjqX5FgUDQINDKcE1rb7rrgt3eZSfL4L+7ql4/RA+9kqFXEsQGgUELoXpIi/DWvtOcEvFqess3mUwNPnJc+ED4QCAQCAQCgaAytRJhd++995KSksKmTZvo168fkydP9kfweDwegoKCGDt27AUbW1sEslgharxcenQamNwOTDr47w5IU0opVjxERUVxcydv/Se5fmjRNUJVVfFSJBAIKqDXwH09we6CZYe8jSTKo5EgMQQGN4aJraBppFe4q415uLy8POx2O2lpaRc+WIAifCB8IBAIBAKBQFCZCxbsVFUlIiKCW2+9lenTp1eIolNVFb1ez6xZs+pNdJ1AcD4YtTCpLVzZArbukjAYoV1z70uuOLUFAkGgI0neFNa5Q7xRcwv3esW7UCO0jvJG1A1pAi2jav+aJzpXCx+A8IFAIBAIBAJBZWql6cT27dsJDw/3z4qWlZXx0Ucf8cUXX+DxePi///s/br31VkJDa1jQ5iITyPW7QkND61U9wIaC7wXVpIPmyZHodLqAFetkWUaj0dS1GQKBoB4SZoCH+sId3b0NJ3y16YxabyTxxbjmaTSagL4v1wbCB8IHAoFAIBAIBJW54P5tRUVF3HPPPezbt8+/7Ntvv+Xxxx+nWbNm9OvXj3nz5vG///3vQjdVawTyA2FQUBBhYWF1bUaDJiwsjKCgoLo247yRJAlZFq0bBQLB6UiStyZdtBligrw16YL0p+rSXaQJiujo6AZfl1X4QPhAIBAIBAKBoDIXrFxZLBasVivt27cHwOl0Mn/+fEaMGMFrr71GUFAQjRs35ptvvuHmm2+uF6mxdru9rk04b7KysrDb7TRr1qyuTWmwpKenYzKZSEpKqmtTzguPxxPQjVcEAsHlhd1ux+12YzQa69qUOkP4QPhAIBAIBAKBoDIXHGbjdrtRFMUftXb8+HEyMzMZMGAA4eHh6PV62rRpQ2lp6QUbKxANA+oDHo9HHAOBQCCoJYqLi8nLy6trM+oU4QPhA4FAIBAIBILKXLBgp9frsdvtHD9+HFVV2b59O2VlZXTq1AlJklBVFafTKequ1SL1IUqxISNJkjgGAoFAIBAIBAKBQCAQCC4aF5wSGxUVxZAhQ5gxYwY9evRg1apVNG/enK5duwKgKAqbN28mJSWl3ogcgZxukZCQgKqqdW1Gg6ZJkyb15lw+HzQaDTqdrq7NEAgEAsB7T27odTWFD4QPBAKBQCAQCCpzwYKd2Wxm1qxZvP322/z5558MHDiQmTNn+kUxl8tFXl4eV1111QUbW1u43e66NuG8sVqtuN1uIiMj69qUBktJSQk6na7edD0+V0RatUAgqE9ERUU1+Iko4QPhA4FAIBAIBILK1Eq71MTERJ544glcLhcajQa9Xu+PQDIYDDz99NP1KqInkAW74uJi7Ha7EOzqkPz8fMxmc8AKdoqi4PF46toMgUAgACAnJwe73U7jxo3r2pQ6Q/hA+EAgEAgEAoGgMrUi2EmShE6nq1KUkyRJ1K+rZcQMdN2iqqo4BgKBQFBLuFwunE5nXZtRpwgfCB8IBAKBQCAQVKZWBLtAI5BrpJhMJjQaTV2b0aAJDg7GYDDUtRnnjSRJAf0bEAgElx+BXBe0thA+ED4QCAQCgUAgKE+DFOz0en1dm3DehIeHi/pjdUxMTExAC16yLAvRVyAQ1BuioqIafJq+8IHwgUAgEAgEAkFlAld1uAACOeUiPz+frKysujajQZOVlUVBQUFdm3HeKIoS0HUcBQLB5YWI+hU+AOEDgUAgEAgEgso0yCejQI5Qczqd2O32ujajQWOz2XA4HHVtxnkjavAJBIL6RF5eHpmZmXVtRp0ifCB8IBAIBAKBQFCZBinYBTKSJIkaL3WMLMviGAgEAkEtIiYRhA9A+EAgEAgEAoGgPKKGXYARERFBaGhoXZvRoImLiwvoGnCyLKPVNsifvkAgqIdotdoqu8w3JIQPhA8EAoFAIBAIKtMg39oDOTpKq9UGtP2XA1qtNqAFO3H+CASC+kRcXFyDj6wSPhA+EAgEAoFAIKhMg0yJDeT6Yzk5OWRkZNS1GQ2a48ePk5OTU9dmnDcej0c0nRAIBPWGgoICsrOz69qMOkX4QPhAIBAIBAKBoDINMsJOIBAIBAJB/cBms2Gz2erajDpF+ED4QCAQCAQCgaAyDTLCLpAR9cfqnkBPiQWRFisQCAQCgUAgEAgEAkF9pkEqPyaTqa5NOG8SExNFjZc6pkmTJgEteInC3gJB3aCqKh6PB1VVkWX5jB2nVVX1rytJEhqNJqCvOdUREhIS0Pfl2kD4QPhAIBAIBAKBoDINUrBzOp11bcJ5U1BQgNPpJCEhoa5NabBkZ2ej1+uJioqqa1POC1HDTiCoG/bv38/cuXM5ePAgPXv25JVXXqlyveLiYt588022b9+OVqvluuuuY/To0ZdtdLXZbEZRlLo2o04RPhA+EAgEAoFAIKhMg0yJ9Xg8dW3CeWO1WiktLa1rMxo0JSUlWK3WujbjvFFVVbwUCQR1QGRkJFOnTmX06NEUFxdXuY6qqixevJi9e/fyzDPPcOutt/Lyyy9z8uTJS2ztpSMvL4/MzMy6NqNOET4QPhAIBAKBQCCoTIMU7AQCgUAguNRER0czYsQIGjdufMZ1VFVlxYoVjB07liZNmtCjRw9SUlLYtGnTpTP0EiOifoUPQPhAIBAIBAKBoDKXZ35NNQRyWlFISAhGo7GuzWjQREREoNfr69qM80aW5YBvmiEQBAJV1RutSS26wsJCIiIikCQJvV5PTEzMaRF2JSUlHDx4ELfbzdGjRykrK/PX/zIYDCiKgsvlAry/eYPBgN1u99uk1+uRJAmHw+G3y2Aw4HK5/FHoWq0WrVaLw+Hwf6+mY8Nf5SfONrbdbicvLw+n00lhYWGVYzscDn9U8LnYbbfb/f4yGo14PB7/2BqNBp1Oh9PpPOvYRqMRp9N5Tj45n7Hz8vJQFIXCwkKcTme1/j5fn1zsY+njfOzOy8tDVVVyc3MrjK3T6Sp0j61qbI1G4/ehQCAQCAQCweVC4CpXF0AgC3ZBQUGi6UQdEx4ejiwHbnCqJEkBbb9AECgUFhbyyiuv4HQ66dq1K1dddVWNGr5oNBoURanQfKLyfaugoICVK1dit9sxGo1YrVbWrFlzsXblorJ//36Ki4vp3r17XZtSZwgfXLgPunTpIiY0BQKBQCAQXFYErnJ1AfhmcwOR3NxcHA4HTZo0qWtTGiwZGRmYTKaAbfwh0o4EgkuD2Wxm9OjRKIpCbGxsjSJbJUkiNTWVw4cPo6oqVquVI0eOMHHixArrpaam8ve//x34K5IvUDvJLlu2jOPHj3PbbbcF7D5cKMIHF+4DX/dlgUAgEAgEgsuFBinYBXLKRPmUHkHd4HK5ahQlU58RUZoCwcXHaDTSu3dv/98lJSVs3ryZnTt3kpmZyfLly+ncuTPh4eG88cYbtG/fnqFDhzJhwgRefPFFGjVqxKFDh9Dr9bRr167C2JIkXRbCjqqq/rRHWZYvi306V4QPhA8EAoFAIBAIqqJBCnaB/CAoy7KYQa5jNBqNOAYCgeCcKSkp4eeff8Zms9G8eXO+//57kpOTCQ8Pr1CftH///siyzE8//UR4eDgvvfQSkZGRdWz9xaNTp060bNmyrs2oU4QPhA8EAoFAIBAIKiOpDSjURlVV3nnnHVJTUxk1alTACXeqqvoLNptMpoCz/3JAVVVsNpu/gHYgHgOXy8Udd9zB008/TUxMTF2bIxAIBAKBQCAQCAQCgaASDTJMKJBTYl0uV0DX4LsccDgcAZ2W7CtkLxAIBBcDVVVRFAW3243b7a5wz/U10Sj/me96VP57Ho+nwnVKVVUsFgtLlixh+/bt9eYaVnlfq7LL93nl/fH5oap9zcnJYeHChZw4caKCf8qP4/u7vH89Hs8lf8Y5mw+q28/LxQcCgUAgEAgEF4MGKdgFsthSWFhITk5OXZvRoMnOzqawsLCuzThvFEXB4/HUtRkCgeAyRVEUFi5cyOTJk+nRowdLly4FvOLK0aNHeeCBB7jhhhu48cYb+eCDD/yTUDk5Ofzzn/9kypQp3HrrraxZs6aC8LJ3716Ki4vZsGED+fn5dbJvlXE6nTz33HNMnDiRoUOHsnv37gqfK4rCl19+SYcOHdi4caN/+a5du5gxYwZTpkzh3nvv5ejRo/7PVFXlhx9+wGQysWrVKv/1+vjx40yePJlDhw4BkJ6eTpcuXVi0aBEAbrebmTNn8u23317cna7E2Xxw7Ngx7rvvPqZMmcLtt99eQWy9nHwgEAgEAoFAcDFokIKdQCAQCASCi4MkSbRo0YIZM2aQnJyM1Wr1f/bpp5/icDh49tlnueOOO3j33Xc5duwYbrebf//731itVl544QWuuOIKnnvuOSwWi/+7ISEhHDx4EEmSMJvNdbFrpyHLMn369OGee+7B5XJVmBBUVZWDBw+yaNEiDAYDTqcT8NYyfOGFF+jRowcvvPACUVFRzJs3zy9KSZJEXFwcGzduJCIiwl96ISEhgeLiYvbt24eqquzatQuADRs2oKoqJ0+e5MCBA6SlpdULH9jtdl599VXi4+N58cUX6dChAy+88AIej+ey84FAIBAIBALBxaBBCnaB3DDAYDBgMpnq2owGjdlsxmAw1LUZ583l0l1SIBDUTyRJokOHDgwYMIDw8PAKn5lMJoxGI2FhYYSEhBAcHIxOp8PpdLJq1SquvfZaUlJSGDZsGBqNxi/ISJJEs2bNePjhh7nxxhvrzX1Qq9UyYMAAevbsiV6vr/CZy+Xi/fffZ/z48cTGxvqXHzt2jCNHjnDdddfRqFEjJk+ezK5duzh58iTg3ddhw4bx8MMPM3LkSP8zi1arpWPHjmzduhVFUfjjjz+4+eabOXz4MKWlpezduxeA1q1bX6K9x29XVT4oKirijz/+YNKkSaSkpDBhwgRyc3M5fPjwZecDgUAgEAgEgotBvVWu3G43K1as4L777mPWrFls27atypokiqKwZ88e/vWvf3HnnXfy+uuvU1ZWdtaxKz9UBxJRUVEkJibWtRkNmoSEBKKiourajPNGlmW02gbZIFogEFwCzjYpMGHCBE6cOMENN9zAP/7xD0aNGkVCQgKKomCxWAgODkaSJIKCgggNDSUrK8v/XV+zH51OV28mHc60r4qisHjxYmRZ5oorrqiwjsViQa/Xo9PpAIiNjcXlclFUVORfR6PRYDAY0Gg0Fb7bt29fNmzYQHFxMfv37+eKK64gODiYffv2sXPnTtq0aXPJn3HO5ANfkyxf9+OIiAi0Wi25ubmXnQ8EAoFAIBAILgb1UrBTVZXff/+dF154gTFjxtC2bVvuv/9+MjMzT1tv165dPPjgg7Ru3Zrp06fTpk2baiPofGkpgUheXt5pfhBcWjIzM+tN/aTzwVccXCAQCC41CxYsIDExkeeff55HH32UX375hT179gBeQc7XFMfXyCBQJxdOnjzJZ599xqRJk/B4PHg8Hmw2Gy6Xy7+fPjweD5IkodFozjqmL8owOzubbdu2AdCsWTOaNWvGjh07+O233+jXr99F3a9zwSfk+fbVl+6q0WgajA8EAoFAIBAILoR6KdgBLFu2jGHDhjF48GCuuuoqEhMT+e23305b7/PPP6dTp0507NiRoKAgevToUW2qTCB3D3M6ndhstro2o0Fjs9kCulOv6BIrEAjqAt9k3IABA2jRogU9evQgISGBXbt2odVqiY2NJSMjA4D8/Hzy8vJo1qxZHVt9fhQVFVFYWMjs2bOZPn0627dv54UXXmDDhg1ERkaiKAoFBQUAHDhwALPZTFxcXLXjJiUlER8fz/z582nWrBn/v717jY2i+sM4/szOboGWdkGpQC/p0kBbJIC0BlOQBhMCWCSARAlIFQ2EmiAXRaPByxshWLkkBiVIlFjRkojCC4FKQYKISBDF0kKxtLUXQKAIdWkp3cv8X5Ddf4uAVats1+/n3XbnnDlzZl80z5zfGbvdrvT0dO3fv1+VlZVKS0v7py+t3SIjIxUZGRl8UVZdXZ08Ho+SkpL+M3MAAADwd4TMo+vWAYJlWaqqqtLDDz8sm82mbt26yeVy6aeffvpdm8OHD8vj8ej06dNyu91yOp1688032+yb4/P51NzcLMuydPXqVbndbl24cEGGYSgiIkJ+vz+4SbJpmsH9dPx+vwzDCJZseDweWZYlm82miIgIeTye4BNju90u0zTV0tIiy7Ju2ndERESwTET6f3lu674dDoe8Xm+bJ852u11Xr15VQ0ND8Bosy7rpuNvTd2Dcgb6vH7fX65VlWcHzezye4JzY7XbZbLZgcGUYhrp06dLuOWnd9/XzbbfbZRhGcNx/tu/AnATuU+s5CZRS/Z176Xa75fP51NTU1OZe3qhvh8Mhn88XXNF2q/m+1bhvdi/b23fr+Q60IbQD8E+pqalRWVmZfvnlF5WUlCg+Pl7Dhw9Xenq6Nm3apOjoaJ06dUonTpzQwoULFRERoSlTpig/P1/dunXTvn37lJiYKJfLdbsv5Q8VFxerpqZGFy9e1MGDB2WaplJSUvTJJ59IuvbyhVmzZmn+/Pm69957ZRiGRowYoVWrVik7O1v5+fkaM2aMevbs+YfncjqdSk1N1YYNG7Ru3TpJUlJSkoqLixUTE6O+ffvelnLhG81BamqqHnroIb399tuaMWOGtm7dqoyMDPXu3Vsejyfs5gAAAKCjhUxgd+XKFe3bt08+n09xcXHy+Xyy2WzBcorWZRXXt0tOTtaKFSvU3Nysp59+Wnv37tWkSZOCx5w4cUJr1qxRY2OjTpw4ocjISBUVFbUp1wj0faO/BUpsA+FJe9r92b7b2+7s2bNqbm6Wy+Xq8L5btwuFOQnVvqurq4MrAf7t+e6Ivi3L0pEjRzr1KkEAoa2iokI7duxQ//79dfHiRe3atUvp6el69tlntX37du3evVuRkZF66623NHjwYNlsNk2dOlVRUVHauXOnEhISlJub2yle8HPo0CH9+OOPGjlypEpKSuRwOJSWlhZ80URLS4smT56stLS04H5uL774ogoKClRUVKTx48dr8uTJ7QqZbDabpkyZIknKyMiQJCUkJGj69Om644471KtXr3/oKm/tZnOQm5urTz/9VF988YWGDBmiqVOnBh/ChdscAAAAdDTDCpFlNg0NDXr//ffl8Xg0aNAg7dixQ4MGDdLcuXPl8Xi0cOFC3XfffZo1a1awjd/vV05OjlJTU7VkyRJ5vV4tWrRIQ4YMUW5ubpvjfD6fLMvSu+++K5fLpXHjxt2Gq/z7CgsLVVtbq9mzZ/ME+TYI/Ib69eunsWPH3u7h/CVer1fz58/X0qVL27y5EAAAAAAAhIaQWWEXExOjBQsWBFcPmaapd955R/fff7/Onz+vsrIyLV68WD6fT3v27FF8fLzS0tI0efJkvffeezp69KguX76s0tLSNqGedO1pbGClUaC8M1B22ZkExh8oI+1s4w8Hre9BZ/wNSddW2QVWrwIAAAAAgNATMoFdoJwvICsrS7W1tVq+fLkcDoeee+45JSUlyefzadu2bcrMzFRaWpoefPBBnTt3TsuXL5fdbtecOXM0dOjQm55n2LBh7dojJVQlJyfrzjvvvN3D+E/r7L8hm82mcePG/eHLWQAAAAAAwO0RMiWx17vRsG62j92N3Gj10PVtO9sKo84+/nAQDveg9TV0xvEDAAAAABDuQjawAwAAAAAAAP6LbLd7AAAAAB3BsixVVlbqt99++1fO5/f7VV1drYaGhg7t1+12q7q6Wl6vt0P7BQAAQOdBYAcAAMKCZVmaOHGiioqK/pXzNTU16cknn9TWrVslSeXl5Vq7dq0uXrz4t/otKipSTk6O6uvrO2CUAAAA6IwI7AAAAP6Cbt26ac2aNcrOzpYkVVVVKT8/v8NX3AEAAOC/J2TeEgsAANCR/H6/qqqqtGnTJtXU1CghIUHTpk3TgAEDZBiGDhw4oC+//FKjR4/Wtm3b1NDQoPHjx2v8+PFyOByyLEs1NTX68MMPdfr0aY0YMUJRUVEqKyvTSy+9JI/Ho88++0xZWVlqamrS+vXrVVFRoeeff17R0dGaPXu2oqOjtXnzZs2bN0+xsbGSpC1btqiurk5z586Vw+FQfX29CgoKdOzYMQ0ePFiRkZFtXhDU0tKiAwcO6PPPP5fb7dbw4cM1ZcoU9ejRg5cHAQAAhClW2AEAgLBUUVGhRx99VCUlJRo4cKBKS0s1c+ZMlZWVSZKqq6u1fv16LV++XE6nU6ZpasGCBfrmm28kSRcuXNDs2bO1f/9+paSk6Ntvv9XLL7+s3bt3S5I8Ho927dqlyspKRUVFacCAAerevbvS09OVmZmp2NhYnTlzRjt37lRjY2NwXCUlJdq/f7/8fr+uXr2qxYsXq6CgQP3791dlZaXWrFmjlpYWSdfKfDdu3Kh58+bJNE0lJycrPz9fS5Yskcfj+ZdnFAAAAP8WVtgBAICwVFBQoK5du2rFihWKi4vTY489pkceeUQff/yxXnvtNUmS1+vVokWL9MADD6ixsVF1dXUqLCxUVlaWCgsLVV9fr40bN+ruu+9WfX29ysvLg2Faa7169dLo0aO1Z88eTZ8+XS6XS9K1Mtlb+e6773T48GGtXr1aY8aMUVNTk5555hkdOXJEknTq1Cl98MEHWrBggWbNmiXTNJWVlaX58+fr6NGjysjI6NA5AwAAQGggsAMAAGHp4MGDGjlypHr37i3DMNSrVy+NGDFCJSUlampqkiQlJycrJSVFhmEoKipK8fHxOn/+vPx+v0pLS9WnTx8NHDgw2D4zM1NfffVVh42xtLRUTqdTw4YNk2EYioyM1KhRo4KrACsqKlRbW6tDhw7p559/liRduXJFv/76q8rLywnsAAAAwhSBHQAACEs+n0+maQb3eTMMQ3a7XT6fL3iMw+GQ3W5v873f7w+2t9vtbfaJa91fexiGIb/f32ZPutalrF6vV4ZhyDTNNmMInMPr9cqyLEVERMhmu7aTSVRUlJ544gmlpqb+qfkAAABA50FgBwAAwlJqaqpKS0vldrvldDrV3Nys4uJiJSUlqUuXLrdsa7PZlJSUpL179+rMmTPq27evGhsbVVZWFgz0rmea5u/CuZiYGF2+fFmNjY2yLEtXrlxReXl5MDR0uVxyu92qqqpSjx495PF4VFpaqubmZklSXFyc7rrrLk2YMEFjx46VzWaTZVny+XzBAA8AAADhh8AOAACEpZycHE2bNk2rV6/WyJEj9fXXX+vkyZNavHixIiIibtnWMAxlZ2drw4YNev311zVp0iQdO3ZMBw4cUHJy8g3bJCQk6MKFCyosLNSgQYOUmpqqlJQUmaaptWvXauLEifr+++91+PBh3XPPPZKkUaNGqXfv3lq2bJmeeuop1dXVafv27cFAMSUlRdnZ2Xr11Vd1+vRpJSYm6tKlSyovL1dOTo4SExM7dM4AAAAQGng0CwAAwobL5VL37t0lSUOHDtW6det0/PhxLVu2TMeOHVNeXp4yMzNlGIaio6OVkJAQLEeVpNjYWPXp0yfY18qVK3Xu3Dnl5eXp7NmzGjdunJxOp6Rrq/ASExMVExMjSerXr59yc3O1efNmvfLKKzpy5IicTqdWrVqlmpoa5eXlyefzaebMmYqPjw+OYeXKlYqOjlZeXp5++OEHzZ07VwMGDJBpmjJNUwsXLtScOXO0ZcsWLV26VB999JHsdntwHAAAAAg/htW6bgMAAKCTav0vTWAPuJv9m2MYxi2PD3x/6dIlRUdHyzAM1dXV6fHHH9eECRP0wgsv/K5968+WZbVrr7vr2/2V7wEAABB+KIkFAABh4Ubh1a0CrfYc/8Ybb6i4uFhdu3ZVbW2t4uLiNGPGjBse2/rzn30xxd/5HgAAAOGHFXYAAAA3YFmWTp48qePHj+vy5cvq2bOnMjIyFBsbS4gGAACAfxSBHQAAAAAAABBCeOkEAAAAAAAAEEII7AAAAAAAAIAQQmAHAAAAAAAAhBACOwAAAAAAACCEENgBAAAAAAAAIYTADgAAAAAAAAghBHYAAAAAAABACCGwAwAAAAAAAEIIgR0AAAAAAAAQQgjsAAAAAAAAgBBCYAcAAAAAAACEEAI7AAAAAAAAIIQQ2AEAAAAAAAAhhMAOAAAAAAAACCH/A/VhXS0ikWMxAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# figure size in inches optional\n", + "rcParams['figure.figsize'] = 16, 10\n", + "\n", + "# path to images\n", + "plot1 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoSstSkew_diagnostic_divedown01.png\")\n", + "plot2 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoSstSkew_diagnostic_divedown02.png\")\n", + "plot3 = os.path.join(demo_output_directory,\"basicTestEnso/ENSO_proc/cmip5_historical_ENSO_proc_ACCESS1-0_r1i1p1_EnsoSstSkew_diagnostic_divedown03.png\")\n", + "\n", + "# display images\n", + "fig, ax = plt.subplots(1,3); ax[0].axis('off'); ax[1].axis('off'); ax[2].axis('off')\n", + "ax[0].imshow(mpimg.imread(plot1))\n", + "ax[1].imshow(mpimg.imread(plot2))\n", + "ax[2].imshow(mpimg.imread(plot3))" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:pmp_test] *", + "display_name": "pmp_devel_20241202", "language": "python", - "name": "conda-env-pmp_test-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1901,7 +2407,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/doc/jupyter/Demo/basic_enso_param.py.in b/doc/jupyter/Demo/basic_enso_param.py.in index 238312014..94904fa24 100644 --- a/doc/jupyter/Demo/basic_enso_param.py.in +++ b/doc/jupyter/Demo/basic_enso_param.py.in @@ -27,4 +27,4 @@ results_dir = os.path.join('$OUTPUT_DIR$',case_id, metricsCollection) json_name = '%(mip)_%(exp)_%(metricsCollection)_%(case_id)_%(model)_%(realization)' netcdf_name = json_name -nc_out = False +nc_out = True diff --git a/pcmdi_metrics/enso/enso_driver.py b/pcmdi_metrics/enso/enso_driver.py index 737df88b0..0c89a5053 100755 --- a/pcmdi_metrics/enso/enso_driver.py +++ b/pcmdi_metrics/enso/enso_driver.py @@ -12,13 +12,16 @@ import shapely # noqa: F401 # isort: on -import cdms2 +from os.path import join as OSpath__join + +# import cdms2 from EnsoMetrics.EnsoCollectionsLib import ( CmipVariables, ReferenceObservations, defCollection, ) from EnsoMetrics.EnsoComputeMetricsLib import ComputeCollection +from EnsoPlots.EnsoMetricPlot import main_plotter from pcmdi_metrics import resources from pcmdi_metrics.enso.lib import ( @@ -35,6 +38,7 @@ # OpenBLAS blas_thread_init: pthread_create failed for thread XX of 96: Resource temporarily unavailable os.environ["OPENBLAS_NUM_THREADS"] = "1" + # ================================================= # Collect user defined options # ------------------------------------------------- @@ -94,6 +98,8 @@ ) ) netcdf_path = outdir(output_type="diagnostic_results") +fig_path = outdir(output_type="graphics") +json_path = outdir(output_type="metrics") json_name_template = param.process_templated_argument("json_name") netcdf_name_template = param.process_templated_argument("netcdf_name") @@ -119,6 +125,11 @@ obs_catalogue_json = param.obs_catalogue +path_in_json = json_path +path_in_nc = netcdf_path +pattern = "_".join([mip, exp, mc_name, case_id]) +path_out = fig_path + # ================================================= # Prepare loop iteration # ------------------------------------------------- @@ -282,7 +293,7 @@ # ================================================= # Loop for Models # ------------------------------------------------- -print("Process start: %s" % time.ctime()) +print(f"Process start:{time.ctime()}") dict_metric, dict_dive = dict(), dict() print("models:", models) @@ -568,39 +579,157 @@ else: obs_interpreter = None - # Computes the metric collection - print("\n### Compute the metric collection ###\n") - cdms2.setAutoBounds("on") - dict_metric[mod][run], dict_dive[mod][run] = ComputeCollection( - mc_name, - dictDatasets, - mod_run, - netcdf=param.nc_out, - netcdf_name=netcdf, - debug=debug, - obs_interpreter=obs_interpreter, + compute_metrics = True # developper switch for testing + + if compute_metrics: + # Computes the metric collection + print("\n### Compute the metric collection ###\n") + # cdms2.setAutoBounds("on") + dict_metric[mod][run], dict_dive[mod][run] = ComputeCollection( + mc_name, + dictDatasets, + mod_run, + netcdf=param.nc_out, + netcdf_name=netcdf, + debug=debug, + obs_interpreter=obs_interpreter, + ) + + if debug: + print("file_name:", file_name) + print("list_files:", list_files) + print("netcdf_name:", netcdf_name) + print("json_name:", json_name) + print("dict_metric:") + print(json.dumps(dict_metric, indent=4, sort_keys=True)) + + # OUTPUT METRICS TO JSON FILE (per simulation) + metrics_to_json( + mc_name, + dict_obs, + dict_metric, + dict_dive, + egg_pth, + outdir, + json_name, + mod=mod, + run=run, + ) + else: + print("pass metrics computing") + + # + # Figure + # + + print("figure plotting start") + + metrics = sorted( + defCollection(mc_name)["metrics_list"].keys(), key=lambda v: v.upper() ) + print("metrics:", metrics) - if debug: - print("file_name:", file_name) - print("list_files:", list_files) - print("netcdf_name:", netcdf_name) - print("json_name:", json_name) - print("dict_metric:") - print(json.dumps(dict_metric, indent=4, sort_keys=True)) - - # OUTPUT METRICS TO JSON FILE (per simulation) - metrics_to_json( - mc_name, - dict_obs, - dict_metric, - dict_dive, - egg_pth, - outdir, - json_name, - mod=mod, - run=run, + filename_js = OSpath__join( + outdir(output_type="diagnostics"), json_name + ".json" ) + print("filename_js:", filename_js) + # data_json = dict_metric + with open(filename_js) as ff: + data_json = json.load(ff)["RESULTS"]["model"][mod][run] + + for met in metrics: + print("met:", met) + # get NetCDF file name + filename_nc = OSpath__join( + path_in_nc, pattern + "_" + mod + "_" + run + "_" + met + ".nc" + ) + print("filename_nc:", filename_nc) + if os.path.exists(filename_nc): + # get diagnostic values for the given model and observations + if mc_name == "ENSO_tel" and "Map" in met: + dict_dia = data_json["value"][met + "Corr"]["diagnostic"] + diagnostic_values = dict( + (key1, None) for key1 in dict_dia.keys() + ) + diagnostic_units = "" + else: + dict_dia = data_json["value"][met]["diagnostic"] + diagnostic_values = dict( + (key1, dict_dia[key1]["value"]) for key1 in dict_dia.keys() + ) + diagnostic_units = data_json["metadata"]["metrics"][met][ + "diagnostic" + ]["units"] + # get metric values computed with the given model and observations + if mc_name == "ENSO_tel" and "Map" in met: + list1, list2 = [met + "Corr", met + "Rmse"], [ + "diagnostic", + "metric", + ] + dict_met = data_json["value"] + metric_values = dict( + ( + key1, + { + mod: [ + dict_met[su][ty][key1]["value"] + for su, ty in zip(list1, list2) + ] + }, + ) + for key1 in dict_met[list1[0]]["metric"].keys() + ) + metric_units = [ + data_json["metadata"]["metrics"][su]["metric"]["units"] + for su in list1 + ] + else: + dict_met = data_json["value"][met]["metric"] + metric_values = dict( + (key1, {mod: dict_met[key1]["value"]}) + for key1 in dict_met.keys() + ) + metric_units = data_json["metadata"]["metrics"][met]["metric"][ + "units" + ] + # figure name + figure_name = "_".join([mip, exp, mc_name, mod, run, met]) + print("figure_name:", figure_name) + # this function needs: + # - the name of the metric collection: metric_collection + # - the name of the metric: metric + # - the name of the model: modname (!!!!! this must be the name given when computed because it is the name used + # for in the netCDF files and in the json file !!!!!) + # - name of the exp: exp + # - name of the netCDF file name and path: filename_nc + # - a dictionary containing the diagnostic values: diagnostic_values (e.g., {"ERA-Interim": 1, "Tropflux": 1.1, + # modname: 1.5}) + # - the diagnostic units: diagnostic_units + # - a dictionary containing the metric values: metric_values (e.g., {"ERA-Interim": {modname: 1.5}, + # "Tropflux": {modname: 1.36}}) + # - the metric units: metric_units + # - (optional) the path where to save the plots: path_out + # - (optional) the name of the plots: name_png + + main_plotter( + mc_name, + met, + mod, + exp, + filename_nc, + diagnostic_values, + diagnostic_units, + metric_values, + metric_units, + member=run, + path_png=path_out, + name_png=figure_name, + ) + + print("figure plotting done") + + else: + print("file not found:", filename_nc) except Exception as e: print("failed for ", mod, run) @@ -609,7 +738,7 @@ pass print("PMPdriver: model loop end") -print("Process end: %s" % time.ctime()) +print(f"Process end: {time.ctime()}") # ================================================= # OUTPUT METRICS TO JSON FILE (for all simulations)