diff --git a/LAND-DIAGS_README.md b/LAND-DIAGS_README.md new file mode 100644 index 000000000..1bafaaf39 --- /dev/null +++ b/LAND-DIAGS_README.md @@ -0,0 +1,81 @@ +### Here's a quick start that you can use for Land Diagnostics +#### Download the adf repo +On casper: +1. Navigate to the directory where you want the adf (e.g., `cd ~`) +2. Clone the ADF +`git clone https://github.com/NCAR/ADF.git` +3. Set your personal repository as the upstream repo +``` +cd ADF +git remote add upstream https://github.com//ADF.git +``` +4. Switch to the clm-diags branch +`git switch -c clm-diags origin/clm-diags` + +#### Set up your computing environment +5. Create a conda environment. On NCAR's CISL machines (derecho and casper), these can be loaded by running the following on the command line: +``` +module load conda +conda env create -f env/ldf_v0.0.yaml +conda activate ldf_v0.0 +``` + +**Note** This is somewhat redundant, as it's a clone of cupid-analysis, but land diagnostics need the latest version of uxarray (25.3.0), and this will prevent overwriting your other conda environments. + +Also, along with these python requirements, the `ncrcat` NetCDF Operator (NCO) is also needed. On the CISL machines this can be loaded by simply running the following on the command line: + +``` +module load nco +``` + +## Running ADF diagnostics + +Detailed instructions for users and developers are availabe on this repository's [wiki](https://github.com/NCAR/ADF/wiki). + +You'll have to add your username to the appropriate config file, but after that, for a quick try of land diagnostics + +`./run_adf_diag config_clm_unstructured_plots.yaml` + +This should generate a collection of time series files, climatology (climo) files, re-gridded climo files, and example ADF diagnostic figures, all in their respective directories. + +**NOTE:** If you get NCO failures at the generate timeseries stage that end up causing LDF to fail, see issue [#365](https://github.com/NCAR/ADF/issues/365) + +When additional memory is needed sometimes need to run interactive session on casper: +`execcasper -A P93300041 -l select=1:ncpus=4:mem=64GB` + +## TEST for Land Diags: + +For this branch there are (3) ways to run the ADF: + +1) On native grid with unstructured plotting via Uxarray +2) On native grid but gridded to lat/lon +3) On already lat/lon gridded input files (hist, ts, or climo) + +For (1), the config yaml file will be essentially the same, but with a couple of additional arguments: + - in `diag_basic_info` set the `unstructured_plotting` argument to `true` + - in each of the test and baseline section supply a mesh file in the `mesh_file` argument + + Example yaml file: `config_clm_unstructured_plots.yaml` + +For (2), the config yaml file will need some additional arguments: + - in each of the test and baseline sections, supply the following arguments: + + Weights file: + + `weights_file: /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc` + + Regridding method: + + `regrid_method: 'coservative'` + (Yes, spelled incorectly for a bug in xESMF) + + Lat/lon file: + + `latlon_file: /glade/derecho/scratch/wwieder/ctsm5.3.018_SP_f09_t232_mask/run/ctsm5.3.018_SP_f09_t232_mask.clm2.h0.0001-01.nc` + + NOTE: The regridding method set in `regrid_method` MUST match the method in the weights file + + Example yaml file: `config_clm_native_grid_to_latlon.yaml` + + +: diff --git a/config_clm_BvsI.yml b/config_clm_BvsI.yml new file mode 100644 index 000000000..bab3f2c0c --- /dev/null +++ b/config_clm_BvsI.yml @@ -0,0 +1,299 @@ +#============================== +#config_cam_baseline_example.yaml + +#This is the main CAM diagnostics config file +#for doing comparisons of a CAM run against +#another CAM run, or a CAM baseline simulation. + +#Currently, if one is on NCAR's Casper or +#Cheyenne machine, then only the diagnostic output +#paths are needed, at least to perform a quick test +#run (these are indicated with "MUST EDIT" comments). +#Running these diagnostics on a different machine, +#or with a different, non-example simulation, will +#require additional modifications. +# +#Config file Keywords: +#-------------------- +# +#1. Using ${xxx} will substitute that text with the +# variable referenced by xxx. For example: +# +# cam_case_name: cool_run +# cam_climo_loc: /some/where/${cam_case_name} +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/cool_run +# +# Please note that currently this will only work if the +# variable only exists in one location in the file. +# +#2. Using ${.xxx} will do the same as +# keyword 1 above, but specifies which sub-section the +# variable is coming from, which is necessary for variables +# that are repeated in different subsections. For example: +# +# diag_basic_info: +# cam_climo_loc: /some/where/${diag_cam_climo.start_year} +# +# diag_cam_climo: +# start_year: 1850 +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/1850 +# +#Finally, please note that for both 1 and 2 the keywords must be lowercase. +#This is because future developments will hopefully use other keywords +#that are uppercase. Also please avoid using periods (".") in variable +#names, as this will likely cause issues with the current file parsing +#system. +#-------------------- +# +##============================== +# +# This file doesn't (yet) read environment variables, so the user must +# set this themselves. It is also a good idea to search the doc for 'user' +# to see what default paths are being set for output/working files. +# +# Note that the string 'USER-NAME-NOT-SET' is used in the jupyter script +# to check for a failure to customize +# +user: wwieder #'USER-NAME-NOT-SET' + +#This first set of variables specify basic info used by all diagnostic runs: +diag_basic_info: + + #Does the user want plotting of unstructured (native) grid? + #If "false" or missing, then the ADF expects ALL cases to be on lat/lon grids: + unstructured_plotting: false + + #Is this a model vs observations comparison? + #If "false" or missing, then a model-model comparison is assumed: + compare_obs: false + + #Generate HTML website (assumed false if missing): + #Note: The website files themselves will be located in the path + #specified by "cam_diag_plot_loc", under the "/website" subdirectory, + #where "" is the subdirectory created for this particular diagnostics run + #(usually "case_vs_obs_XXX" or "case_vs_baseline_XXX"). + create_html: true + + #Location of observational datasets: + #Note: this only matters if "compare_obs" is true and the path + #isn't specified in the variable defaults file. + obs_data_loc: /glade/campaign/cgd/amp/amwg/ADF_obs + + #Location where re-gridded and interpolated CAM climatology files are stored: + cam_regrid_loc: /glade/derecho/scratch/${user}/ADF_LatLon/regrid + + #Overwrite CAM re-gridded files? + #If false, or missing, then regridding will be skipped for regridded variables + #that already exist in "cam_regrid_loc": + cam_overwrite_regrid: false + + #Location where diagnostic plots are stored: + cam_diag_plot_loc: /glade/derecho/scratch/${user}/ADF_LatLon/plots + + #Location of ADF variable plotting defaults YAML file: + #If left blank or missing, ADF/lib/adf_variable_defaults.yaml will be used + #Uncomment and change path for custom variable defaults file + defaults_file: lib/ldf_variable_defaults.yaml + + #Longitude line on which to center all lat/lon maps. + #If this config option is missing then the central + #longitude will default to 180 degrees E. + central_longitude: 0 + + #Number of processors on which to run the ADF. + #If this config variable isn't present then + #the ADF defaults to one processor. Also, if + #you set it to "*" then it will default + #to all of the processors available on a + #single node/machine: + num_procs: 1 + + #If set to true, then redo all plots even if they already exist. + #If set to false, then if a plot is found it will be skipped: + redo_plot: true + +#This second set of variables provides info for the CAM simulation(s) being diagnosed: +diag_cam_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0 + + #Calculate climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Overwrite CAM climatology files? + #If false, or not prsent, then already existing climatology files will be skipped: + cam_overwrite_climo: false + + #Location of CAM climatologies (to be created and then used by this script) + cam_climo_loc: /glade/derecho/scratch/${user}/ADF_LatLon/${diag_cam_climo.cam_case_name}/climo + + #Name of CAM case (or CAM run name): + cam_case_name: b.e30_alpha06b.B1850C_LTso.ne30_t232_wgx3.143 + + #Case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: '143' + + #Location of CAM history (h0) files: + cam_hist_loc: /glade/derecho/scratch/hannay/archive/${diag_cam_climo.cam_case_name}/lnd/hist/ + + # SE to FV regridding options + # Leave these blank if not on the native grid + #----------------------------- + # Weights file: + weights_file: /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc + # Regridding method: + regrid_method: 'coservative' + # Lat/lon file: + latlon_file: /glade/derecho/scratch/wwieder/ctsm5.3.018_SP_f09_t232_mask/run/ctsm5.3.018_SP_f09_t232_mask.clm2.h0.0001-01.nc + + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 30 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 40 + + #Do time series files exist? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files? + #WARNING: This can take up a significant amount of space, + # but will save processing time the next time + cam_ts_save: false + + #Overwrite time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF_LatLon/${diag_cam_climo.cam_case_name}/ts + + +#This third set of variables provide info for the CAM baseline climatologies. +#This only matters if "compare_obs" is false: +diag_cam_baseline_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0 + + #Calculate cam baseline climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Overwrite CAM climatology files? + #If false, or not present, then already existing climatology files will be skipped: + cam_overwrite_climo: false + + #Name of CAM baseline case: + cam_case_name: ctsm53019_f09_BNF_hist + + #Baseline case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: '5.3.019_BNF' + + #Location of CAM baseline history (h0) files: + #Example test files + cam_hist_loc: /glade/derecho/scratch/slevis/archive/${diag_cam_baseline_climo.cam_case_name}/lnd/hist/ + + #Location of CAM climatologies (to be created and then used by this script) + cam_climo_loc: /glade/derecho/scratch/${user}/ADF_LatLon/${diag_cam_baseline_climo.cam_case_name}/climo + + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 1850 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 1860 + + #Do time series files need to be generated? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files for baseline run? + #WARNING: This can take up a significant amount of space: + cam_ts_save: true + + #Overwrite baseline time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF_LatLon/${diag_cam_baseline_climo.cam_case_name}/ts + +#+++++++++++++++++++++++++++++++++++++++++++++++++++ +#These variables below only matter if you are using +#a non-standard method, or are adding your own +#diagnostic scripts. +#+++++++++++++++++++++++++++++++++++++++++++++++++++ + +#Note: If you want to pass arguments to a particular script, you can +#do it like so (using the "averaging_example" script in this case): +# - {create_climo_files: {kwargs: {clobber: true}}} + +#Name of time-averaging scripts being used to generate climatologies. +#These scripts must be located in "scripts/averaging": +time_averaging_scripts: + - create_climo_files + +#Name of regridding scripts being used. +#These scripts must be located in "scripts/regridding": +regridding_scripts: + - regrid_and_vert_interp + +#List of analysis scripts being used. +#These scripts must be located in "scripts/analysis": +analysis_scripts: + - lmwg_table + +#List of plotting scripts being used. +#These scripts must be located in "scripts/plotting": +plotting_scripts: + - global_latlon_map + - global_mean_timeseries_lnd + - polar_map + +#List of CAM variables that will be processesd: +#If CVDP is to be run PSL, TREFHT, TS and PRECT (or PRECC and PRECL) should be listed +diag_var_list: + - TSA + - PREC + - ELAI + - GPP + - NPP + - FSDS + - ALTMAX + - ET + - GRAINC_TO_FOOD + - TOTRUNOFF + - DSTFLXT + - MEG_isoprene + +#END OF FILE diff --git a/config_clm_native_grid_to_latlon.yaml b/config_clm_native_grid_to_latlon.yaml new file mode 100644 index 000000000..fb19eed08 --- /dev/null +++ b/config_clm_native_grid_to_latlon.yaml @@ -0,0 +1,328 @@ +#============================== +#config_cam_baseline_example.yaml + +#This is the main CAM diagnostics config file +#for doing comparisons of a CAM run against +#another CAM run, or a CAM baseline simulation. + +#Currently, if one is on NCAR's Casper or +#Cheyenne machine, then only the diagnostic output +#paths are needed, at least to perform a quick test +#run (these are indicated with "MUST EDIT" comments). +#Running these diagnostics on a different machine, +#or with a different, non-example simulation, will +#require additional modifications. +# +#Config file Keywords: +#-------------------- +# +#1. Using ${xxx} will substitute that text with the +# variable referenced by xxx. For example: +# +# cam_case_name: cool_run +# cam_climo_loc: /some/where/${cam_case_name} +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/cool_run +# +# Please note that currently this will only work if the +# variable only exists in one location in the file. +# +#2. Using ${.xxx} will do the same as +# keyword 1 above, but specifies which sub-section the +# variable is coming from, which is necessary for variables +# that are repeated in different subsections. For example: +# +# diag_basic_info: +# cam_climo_loc: /some/where/${diag_cam_climo.start_year} +# +# diag_cam_climo: +# start_year: 1850 +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/1850 +# +#Finally, please note that for both 1 and 2 the keywords must be lowercase. +#This is because future developments will hopefully use other keywords +#that are uppercase. Also please avoid using periods (".") in variable +#names, as this will likely cause issues with the current file parsing +#system. +#-------------------- +# +##============================== +# +# This file doesn't (yet) read environment variables, so the user must +# set this themselves. It is also a good idea to search the doc for 'user' +# to see what default paths are being set for output/working files. +# +# Note that the string 'USER-NAME-NOT-SET' is used in the jupyter script +# to check for a failure to customize +# +user: 'wwieder' #'USER-NAME-NOT-SET' + +#This first set of variables specify basic info used by all diagnostic runs: +diag_basic_info: + + #Does the user want plotting of unstructured (native) grid? + #If "false" or missing, then the ADF expects ALL cases to be on lat/lon grids: + unstructured_plotting: false + + #Is this a model vs observations comparison? + #If "false" or missing, then a model-model comparison is assumed: + compare_obs: false + + #Generate HTML website (assumed false if missing): + #Note: The website files themselves will be located in the path + #specified by "cam_diag_plot_loc", under the "/website" subdirectory, + #where "" is the subdirectory created for this particular diagnostics run + #(usually "case_vs_obs_XXX" or "case_vs_baseline_XXX"). + create_html: true + + #Location of observational datasets: + #Note: this only matters if "compare_obs" is true and the path + #isn't specified in the variable defaults file. + obs_data_loc: /glade/campaign/cgd/amp/amwg/ADF_obs + + #Location where re-gridded and interpolated CAM climatology files are stored: + cam_regrid_loc: /glade/derecho/scratch/${user}/ADF_LatLon2/regrid + + #Overwrite CAM re-gridded files? + #If false, or missing, then regridding will be skipped for regridded variables + #that already exist in "cam_regrid_loc": + cam_overwrite_regrid: false + + #Location where diagnostic plots are stored: + cam_diag_plot_loc: /glade/derecho/scratch/${user}/ADF_LatLon2/plots + + #Location of ADF variable plotting defaults YAML file: + #If left blank or missing, ADF/lib/adf_variable_defaults.yaml will be used + #Uncomment and change path for custom variable defaults file + defaults_file: lib/ldf_variable_defaults.yaml + + #Longitude line on which to center all lat/lon maps. + #If this config option is missing then the central + #longitude will default to 180 degrees E. + central_longitude: 180 + + #Number of processors on which to run the ADF. + #If this config variable isn't present then + #the ADF defaults to one processor. Also, if + #you set it to "*" then it will default + #to all of the processors available on a + #single node/machine: + num_procs: 1 + + #If set to true, then redo all plots even if they already exist. + #If set to false, then if a plot is found it will be skipped: + redo_plot: true + +#This second set of variables provides info for the CAM simulation(s) being diagnosed: +diag_cam_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0 + + #Calculate climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Overwrite CAM climatology files? + #If false, or not prsent, then already existing climatology files will be skipped: + cam_overwrite_climo: false + + #Location of CAM climatologies (to be created and then used by this script) + cam_climo_loc: /glade/derecho/scratch/${user}/ADF_LatLon2/${diag_cam_climo.cam_case_name}/climo + + #Name of CAM case (or CAM run name): + cam_case_name: b.e30_alpha06b.B1850C_LTso.ne30_t232_wgx3.143 + + #Case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: '143' + + #Location of CAM history (h0) files: + cam_hist_loc: /glade/derecho/scratch/hannay/archive/${diag_cam_climo.cam_case_name}/lnd/hist/ + + # SE to FV regridding options + # Leave these blank if not on the native grid + #----------------------------- + # Weights file: + weights_file: /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc + # Regridding method: + regrid_method: 'coservative' + # Lat/lon file: + latlon_file: /glade/derecho/scratch/wwieder/ctsm5.3.018_SP_f09_t232_mask/run/ctsm5.3.018_SP_f09_t232_mask.clm2.h0.0001-01.nc + + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 30 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 40 + + #model year when climatology should start: + #Note: Leaving this entry blank will make time series + # start at the first available year + climo_start_year: 35 + + #model year when climatology should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + climo_end_year: 40 + + #Do time series files exist? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files? + #WARNING: This can take up a significant amount of space, + # but will save processing time the next time + cam_ts_save: false + + #Overwrite time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF_LatLon2/${diag_cam_climo.cam_case_name}/ts + + +#This third set of variables provide info for the CAM baseline climatologies. +#This only matters if "compare_obs" is false: +diag_cam_baseline_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0 + + #Calculate cam baseline climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Overwrite CAM climatology files? + #If false, or not present, then already existing climatology files will be skipped: + cam_overwrite_climo: false + + #Location of CAM climatologies (to be created and then used by this script) + cam_climo_loc: /glade/derecho/scratch/${user}/ADF_LatLon2/${diag_cam_baseline_climo.cam_case_name}/climo + + #Name of CAM baseline case: + cam_case_name: b.e30_alpha06b.B1850C_LTso.ne30_t232_wgx3.139 + + #Baseline case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: '139' + + #Location of CAM baseline history (h0) files: + #Example test files + cam_hist_loc: /glade/derecho/scratch/hannay/archive/${diag_cam_baseline_climo.cam_case_name}/lnd/hist/ + + # SE to FV regridding options + # Leave these blank if not on the native grid + #----------------------------- + # Weights file: + weights_file: /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc + # Regridding method: + regrid_method: 'coservative' + # Lat/lon file: + latlon_file: /glade/derecho/scratch/wwieder/ctsm5.3.018_SP_f09_t232_mask/run/ctsm5.3.018_SP_f09_t232_mask.clm2.h0.0001-01.nc + + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 30 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 40 + + #model year when climatology should start: + #Note: Leaving this entry blank will make time series + # start at first available year. + climo_start_year: 35 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + climo_end_year: 40 + + #Do time series files need to be generated? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files for baseline run? + #WARNING: This can take up a significant amount of space: + cam_ts_save: true + + #Overwrite baseline time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF_LatLon2/${diag_cam_baseline_climo.cam_case_name}/ts + +#+++++++++++++++++++++++++++++++++++++++++++++++++++ +#These variables below only matter if you are using +#a non-standard method, or are adding your own +#diagnostic scripts. +#+++++++++++++++++++++++++++++++++++++++++++++++++++ + +#Note: If you want to pass arguments to a particular script, you can +#do it like so (using the "averaging_example" script in this case): +# - {create_climo_files: {kwargs: {clobber: true}}} + +#Name of time-averaging scripts being used to generate climatologies. +#These scripts must be located in "scripts/averaging": +time_averaging_scripts: + - create_climo_files + +#Name of regridding scripts being used. +#These scripts must be located in "scripts/regridding": +regridding_scripts: + - regrid_and_vert_interp + +#List of analysis scripts being used. +#These scripts must be located in "scripts/analysis": +analysis_scripts: + - lmwg_table + +#List of plotting scripts being used. +#These scripts must be located in "scripts/plotting": +plotting_scripts: + - global_latlon_map + - global_mean_timeseries_lnd + - polar_map + +#List of CAM variables that will be processesd: +#If CVDP is to be run PSL, TREFHT, TS and PRECT (or PRECC and PRECL) should be listed +diag_var_list: + - TSA + #- PREC + #- ELAI + #- GPP + #- NPP + - FSDS + #- ALTMAX + - ET + #- TOTRUNOFF + #- DSTFLXT + #- MEG_isoprene + +#END OF FILE diff --git a/config_clm_structured_plots.yaml b/config_clm_structured_plots.yaml new file mode 100644 index 000000000..12508b64d --- /dev/null +++ b/config_clm_structured_plots.yaml @@ -0,0 +1,364 @@ +#============================== +#config_cam_baseline_example.yaml + +#This is the main CAM diagnostics config file +#for doing comparisons of a CAM run against +#another CAM run, or a CAM baseline simulation. + +#Currently, if one is on NCAR's Casper or +#Cheyenne machine, then only the diagnostic output +#paths are needed, at least to perform a quick test +#run (these are indicated with "MUST EDIT" comments). +#Running these diagnostics on a different machine, +#or with a different, non-example simulation, will +#require additional modifications. +# +#Config file Keywords: +#-------------------- +# +#1. Using ${xxx} will substitute that text with the +# variable referenced by xxx. For example: +# +# cam_case_name: cool_run +# cam_climo_loc: /some/where/${cam_case_name} +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/cool_run +# +# Please note that currently this will only work if the +# variable only exists in one location in the file. +# +#2. Using ${.xxx} will do the same as +# keyword 1 above, but specifies which sub-section the +# variable is coming from, which is necessary for variables +# that are repeated in different subsections. For example: +# +# diag_basic_info: +# cam_climo_loc: /some/where/${diag_cam_climo.start_year} +# +# diag_cam_climo: +# start_year: 1850 +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/1850 +# +#Finally, please note that for both 1 and 2 the keywords must be lowercase. +#This is because future developments will hopefully use other keywords +#that are uppercase. Also please avoid using periods (".") in variable +#names, as this will likely cause issues with the current file parsing +#system. +#-------------------- +# +##============================== +# +# This file doesn't (yet) read environment variables, so the user must +# set this themselves. It is also a good idea to search the doc for 'user' +# to see what default paths are being set for output/working files. +# +# Note that the string 'USER-NAME-NOT-SET' is used in the jupyter script +# to check for a failure to customize +# +user: wwieder #'USER-NAME-NOT-SET' + +#This first set of variables specify basic info used by all diagnostic runs: +diag_basic_info: + + #Does the user want plotting of unstructured (native) grid? + #If "false" or missing, then the ADF expects ALL cases to be on lat/lon grids: + unstructured_plotting: false + + #Is this a model vs observations comparison? + #If "false" or missing, then a model-model comparison is assumed: + compare_obs: false + + #Generate HTML website (assumed false if missing): + #Note: The website files themselves will be located in the path + #specified by "cam_diag_plot_loc", under the "/website" subdirectory, + #where "" is the subdirectory created for this particular diagnostics run + #(usually "case_vs_obs_XXX" or "case_vs_baseline_XXX"). + create_html: true + + #Location of observational datasets: + #Note: this only matters if "compare_obs" is true and the path + #isn't specified in the variable defaults file. + obs_data_loc: /glade/campaign/cgd/amp/amwg/ADF_obs + + #Location where re-gridded and interpolated CAM climatology files are stored: + cam_regrid_loc: /glade/derecho/scratch/${user}/ADF_unstruct/regrid + + #Overwrite CAM re-gridded files? + #If false, or missing, then regridding will be skipped for regridded variables + #that already exist in "cam_regrid_loc": + cam_overwrite_regrid: false + + #Location where diagnostic plots are stored: + cam_diag_plot_loc: /glade/derecho/scratch/${user}/ADF_unstruct/plots + + #Location of ADF variable plotting defaults YAML file: + #If left blank or missing, ADF/lib/adf_variable_defaults.yaml will be used + #Uncomment and change path for custom variable defaults file + defaults_file: lib/ldf_variable_defaults.yaml + + # location of land regions YAML file (only used in regional_climatology plots) + regions_file: lib/regions_lnd.yaml + + #Longitude line on which to center all lat/lon maps. + #If this config option is missing then the central + #longitude will default to 180 degrees E. + central_longitude: 0 + + #Number of processors on which to run the ADF. + #If this config variable isn't present then + #the ADF defaults to one processor. Also, if + #you set it to "*" then it will default + #to all of the processors available on a + #single node/machine: + num_procs: 8 + + #If set to true, then redo all plots even if they already exist. + #If set to false, then if a plot is found it will be skipped: + redo_plot: false + +#This second set of variables provides info for the CAM simulation(s) being diagnosed: +diag_cam_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0a + + #Calculate climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Location of CAM climatologies (to be created and then used by this script) + cam_climo_loc: /glade/derecho/scratch/${user}/ADF_unstruct/${diag_cam_climo.cam_case_name}/climo + + #Overwrite CAM climatology files? + #If false, or not prsent, then already existing climatology files will be skipped: + cam_overwrite_climo: false + + #Name of CAM case (or CAM run name): + cam_case_name: ctsm5.4.CMIP7_ciso_ctsm5.3.075_f09_124_HIST + + #Case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: 'ctsm5.4_124_HIST' + + #Location of CAM history (h0) files: + cam_hist_loc: /glade/derecho/scratch/wwieder/archive/${diag_cam_climo.cam_case_name}/lnd/hist/ + + # If unstructured_plotting, a mesh file is required! + mesh_file: /glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc + + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 1850 + climo_start_year: 2004 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 2023 + climo_end_year: 2023 + + #Do time series files exist? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files? + #WARNING: This can take up a significant amount of space, + # but will save processing time the next time + cam_ts_save: false + + #Overwrite time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF_unstruct/${diag_cam_climo.cam_case_name}/ts + + +#This third set of variables provide info for the CAM baseline climatologies. +#This only matters if "compare_obs" is false: +diag_cam_baseline_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0a + + #Calculate cam baseline climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Overwrite CAM climatology files? + #If false, or not present, then already existing climatology files will be skipped: + cam_overwrite_climo: false + + #Name of CAM baseline case: + cam_case_name: ctsm5.4_5.3.068_PPEcal115f09_118_HIST + + #Baseline case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: 'ctsm5.4_118_HIST' + + #Location of CAM baseline history (h0) files: + #Example test files + cam_hist_loc: /glade/derecho/scratch/wwieder/archive/${diag_cam_baseline_climo.cam_case_name}/lnd/hist/ + + # If unstructured_plotting, a mesh file is required! + mesh_file: /glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc + + #Location of baseline CAM climatologies: + cam_climo_loc: /glade/derecho/scratch/${user}/ADF_unstruct/${diag_cam_baseline_climo.cam_case_name}/climo + + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 1850 + climo_start_year: 2004 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 2023 + climo_end_year: 2023 + + #Do time series files need to be generated? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files for baseline run? + #WARNING: This can take up a significant amount of space: + cam_ts_save: true + + #Overwrite baseline time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF_unstruct/${diag_cam_baseline_climo.cam_case_name}/ts + + + +#+++++++++++++++++++++++++++++++++++++++++++++++++++ +#These variables below only matter if you are using +#a non-standard method, or are adding your own +#diagnostic scripts. +#+++++++++++++++++++++++++++++++++++++++++++++++++++ + +#Note: If you want to pass arguments to a particular script, you can +#do it like so (using the "averaging_example" script in this case): +# - {create_climo_files: {kwargs: {clobber: true}}} + +#Name of time-averaging scripts being used to generate climatologies. +#These scripts must be located in "scripts/averaging": +time_averaging_scripts: + - create_climo_files + +#Name of regridding scripts being used. +#These scripts must be located in "scripts/regridding": +regridding_scripts: + - regrid_and_vert_interp + +#List of analysis scripts being used. +#These scripts must be located in "scripts/analysis": +analysis_scripts: + - lmwg_table + +#List of plotting scripts being used. +#These scripts must be located in "scripts/plotting": +plotting_scripts: + - global_latlon_map + - global_mean_timeseries_lnd + - polar_map + - regional_timeseries + - regional_climatology + +#List of variables that will be processesd: +#Shorter list here, for efficiency of testing +diag_var_list: + - TSA + - PREC + - ELAI + - FSDS + - FLDS + - ASA + - QBOT + - RNET + - FSH + - ET + - FCTR + - FGEV + - FCEV + - QRUNOFF_TO_COUPLER + - SNOWDP + - TOTVEGC + - GPP + - NEE + - NPP + - NBP + - BTRANMN + - TOTECOSYSC + - TOTSOMC_1m + - ALTMAX + - FAREA_BURNED + - DSTFLXT + - MEG_isoprene + - TWS + - GRAINC_TO_FOOD + - C13_GPP_pm + - C13_TOTVEGC_pm + - C14_GPP_pm + - C14_TOTVEGC_pm + +region_list: + - Global + - N Hemisphere Land + #- S Hemisphere Land + - Polar + - Alaskan Arctic + - Canadian Arctic + - Greenland + - Russian Arctic + #- Antarctica + - Alaska + - Northwest Canada + - Central Canada + - Eastern Canada + - Northern Europe + - Western Siberia + - Eastern Siberia + - Western US + - Central US + - Eastern US + - Europe + - Mediterranean + - Central America + - Amazonia + - Central Africa + - Indonesia + - Brazil + - Sahel + - Southern Africa + - India + - Indochina + #- Sahara Desert + - Arabian Peninsula + - Australia + #- Central Asia ## Was Broken... probably because there were two? + #- Mongolia + #- Tibetan Plateau + diff --git a/config_clm_unstructured_plots.yaml b/config_clm_unstructured_plots.yaml new file mode 100644 index 000000000..fdb2a0da1 --- /dev/null +++ b/config_clm_unstructured_plots.yaml @@ -0,0 +1,368 @@ +#============================== +#config_cam_baseline_example.yaml + +#This is the main CAM diagnostics config file +#for doing comparisons of a CAM run against +#another CAM run, or a CAM baseline simulation. + +#Currently, if one is on NCAR's Casper or +#Cheyenne machine, then only the diagnostic output +#paths are needed, at least to perform a quick test +#run (these are indicated with "MUST EDIT" comments). +#Running these diagnostics on a different machine, +#or with a different, non-example simulation, will +#require additional modifications. +# +#Config file Keywords: +#-------------------- +# +#1. Using ${xxx} will substitute that text with the +# variable referenced by xxx. For example: +# +# cam_case_name: cool_run +# cam_climo_loc: /some/where/${cam_case_name} +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/cool_run +# +# Please note that currently this will only work if the +# variable only exists in one location in the file. +# +#2. Using ${.xxx} will do the same as +# keyword 1 above, but specifies which sub-section the +# variable is coming from, which is necessary for variables +# that are repeated in different subsections. For example: +# +# diag_basic_info: +# cam_climo_loc: /some/where/${diag_cam_climo.start_year} +# +# diag_cam_climo: +# start_year: 1850 +# +# will set "cam_climo_loc" in the diagnostics package to: +# /some/where/1850 +# +#Finally, please note that for both 1 and 2 the keywords must be lowercase. +#This is because future developments will hopefully use other keywords +#that are uppercase. Also please avoid using periods (".") in variable +#names, as this will likely cause issues with the current file parsing +#system. +#-------------------- +# +##============================== +# +# This file doesn't (yet) read environment variables, so the user must +# set this themselves. It is also a good idea to search the doc for 'user' +# to see what default paths are being set for output/working files. +# +# Note that the string 'USER-NAME-NOT-SET' is used in the jupyter script +# to check for a failure to customize +# +user: wwieder #'USER-NAME-NOT-SET' + +#This first set of variables specify basic info used by all diagnostic runs: +diag_basic_info: + + #Does the user want plotting of unstructured (native) grid? + #If "false" or missing, then the ADF expects ALL cases to be on lat/lon grids: + unstructured_plotting: true + + #Is this a model vs observations comparison? + #If "false" or missing, then a model-model comparison is assumed: + compare_obs: false + + #Generate HTML website (assumed false if missing): + #Note: The website files themselves will be located in the path + #specified by "cam_diag_plot_loc", under the "/website" subdirectory, + #where "" is the subdirectory created for this particular diagnostics run + #(usually "case_vs_obs_XXX" or "case_vs_baseline_XXX"). + create_html: true + + #Location of observational datasets: + #Note: this only matters if "compare_obs" is true and the path + #isn't specified in the variable defaults file. + obs_data_loc: /glade/campaign/cgd/tss/people/oleson/lnd_diag_data/obs_data + + #Location where re-gridded and interpolated CAM climatology files are stored: + cam_regrid_loc: /glade/derecho/scratch/${user}/ADF_unstruct/regrid + + #Overwrite CAM re-gridded files? + #If false, or missing, then regridding will be skipped for regridded variables + #that already exist in "cam_regrid_loc": + cam_overwrite_regrid: false + + #Location where diagnostic plots are stored: + cam_diag_plot_loc: /glade/derecho/scratch/${user}/ADF_unstruct/plots + + #Location of ADF variable plotting defaults YAML file: + #If left blank or missing, ADF/lib/adf_variable_defaults.yaml will be used + #Uncomment and change path for custom variable defaults file + defaults_file: lib/ldf_variable_defaults.yaml + + # location of land regions YAML file (only used in regional_climatology plots) + regions_file: lib/regions_lnd.yaml + + #Longitude line on which to center all lat/lon maps. + #If this config option is missing then the central + #longitude will default to 180 degrees E. + central_longitude: 0 + + #Number of processors on which to run the ADF. + #If this config variable isn't present then + #the ADF defaults to one processor. Also, if + #you set it to "*" then it will default + #to all of the processors available on a + #single node/machine: + num_procs: 8 + + #If set to true, then redo all plots even if they already exist. + #If set to false, then if a plot is found it will be skipped: + redo_plot: false + +#This second set of variables provides info for the CAM simulation(s) being diagnosed: +diag_cam_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0a + + #Calculate climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Location of CAM climatologies (to be created and then used by this script) + cam_climo_loc: /glade/derecho/scratch/${user}/ADF_unstruct/${diag_cam_climo.cam_case_name}/climo + + #Overwrite CAM climatology files? + #If false, or not prsent, then already existing climatology files will be skipped: + cam_overwrite_climo: false + + #Name of CAM case (or CAM run name): + cam_case_name: b.e30_alpha07c_cesm.B1850C_LTso.ne30_t232_wgx3.247 + + #Case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: 'B_247' + + #Location of CAM history (h0) files: + cam_hist_loc: /glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing/${diag_cam_climo.cam_case_name}/lnd/hist/ + # /glade/derecho/scratch/wwieder/archive/${diag_cam_climo.cam_case_name}/lnd/hist/ + + # If unstructured_plotting, a mesh file is required! + mesh_file: /glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc + + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 1 + climo_start_year: 85 + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 104 + + #Do time series files exist? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files? + #WARNING: This can take up a significant amount of space, + # but will save processing time the next time + cam_ts_save: false + + #Overwrite time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF_unstruct/${diag_cam_climo.cam_case_name}/ts + + +#This third set of variables provide info for the CAM baseline climatologies. +#This only matters if "compare_obs" is false: +diag_cam_baseline_climo: + + # History file list of strings to match + # eg. cam.h0 or ocn.pop.h.ecosys.nday1 or hist_str: [cam.h2,cam.h0] + # Only affects timeseries as everything else uses the created timeseries + # Default: + hist_str: clm2.h0a + + #Calculate cam baseline climatologies? + #If false, the climatology files will not be created: + calc_cam_climo: true + + #Overwrite CAM climatology files? + #If false, or not present, then already existing climatology files will be skipped: + cam_overwrite_climo: false + + #Name of CAM baseline case: + cam_case_name: b.e30_alpha07c_cesm.B1850C_LTso.ne30_t232_wgx3.234 + + #Baseline case nickname + #NOTE: if nickname starts with '0' - nickname must be in quotes! + # ie '026a' as opposed to 026a + #If missing or left blank, will default to cam_case_name + case_nickname: 'B_234' + + #Location of CAM baseline history (h0) files: + #Example test files + cam_hist_loc: /glade/campaign/cesm/development/cross-wg/diagnostic_framework/CESM_output_for_testing/${diag_cam_baseline_climo.cam_case_name}/lnd/hist/ + # /glade/derecho/scratch/wwieder/archive/${diag_cam_baseline_climo.cam_case_name}/lnd/hist/ + + # If unstructured_plotting, a mesh file is required! + mesh_file: /glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc + + #Location of baseline CAM climatologies: + cam_climo_loc: /glade/derecho/scratch/${user}/ADF_unstruct/${diag_cam_baseline_climo.cam_case_name}/climo + + #model year when time series files should start: + #Note: Leaving this entry blank will make time series + # start at earliest available year. + start_year: 1 + climo_start_year: 61 + + #model year when time series files should end: + #Note: Leaving this entry blank will make time series + # end at latest available year. + end_year: 80 + + #Do time series files need to be generated? + #If True, then diagnostics assumes that model files are already time series. + #If False, or if simply not present, then diagnostics will attempt to create + #time series files from history (time-slice) files: + cam_ts_done: false + + #Save interim time series files for baseline run? + #WARNING: This can take up a significant amount of space: + cam_ts_save: true + + #Overwrite baseline time series files, if found? + #If set to false, then time series creation will be skipped if files are found: + cam_overwrite_ts: false + + #Location where time series files are (or will be) stored: + cam_ts_loc: /glade/derecho/scratch/${user}/ADF_unstruct/${diag_cam_baseline_climo.cam_case_name}/ts + + + +#+++++++++++++++++++++++++++++++++++++++++++++++++++ +#These variables below only matter if you are using +#a non-standard method, or are adding your own +#diagnostic scripts. +#+++++++++++++++++++++++++++++++++++++++++++++++++++ + +#Note: If you want to pass arguments to a particular script, you can +#do it like so (using the "averaging_example" script in this case): +# - {create_climo_files: {kwargs: {clobber: true}}} + +#Name of time-averaging scripts being used to generate climatologies. +#These scripts must be located in "scripts/averaging": +time_averaging_scripts: + - create_climo_files + +#Name of regridding scripts being used. +#These scripts must be located in "scripts/regridding": +regridding_scripts: + #- regrid_and_vert_interp + +#List of analysis scripts being used. +#These scripts must be located in "scripts/analysis": +analysis_scripts: + - lmwg_table + +#List of plotting scripts being used. +#These scripts must be located in "scripts/plotting": +plotting_scripts: + - global_latlon_map + - global_mean_timeseries_lnd + - polar_map + - regional_climatology + - regional_timeseries + +#List of variables that will be processesd: +#Shorter list here, for efficiency of testing +diag_var_list: + - TSA + - TV + - PREC + - ELAI + - FSDS + - FLDS + - QBOT + - ASA + - RNET + - FSH + - ET + - FCTR + - FGEV + - FCEV + - QRUNOFF_TO_COUPLER + - SNOWDP + - TOTVEGC + - GPP + - NEE + - NPP + - NBP + - NPP_NUPTAKE + - BTRANMN + - TOTECOSYSC + - TOTSOMC_1m + - ALTMAX + - TWS + - SOILWATER_10CM + - GRAINC_TO_FOOD + - FAREA_BURNED + #- C13_GPP_pm + # - C13_TOTVEGC_pm + #- C14_GPP_pm + #- C14_TOTVEGC_pm + +region_list: + - Global + - N Hemisphere Land + - S Hemisphere Land + - Polar + - Alaskan Arctic + - Canadian Arctic + - Greenland + - Russian Arctic + # - Antarctica + - Alaska + - Northwest Canada + - Central Canada + - Eastern Canada + - Northern Europe + - Western Siberia + - Eastern Siberia + - Western US + - Central US + - Eastern US + - CONUS + - Europe + - Mediterranean + - Central America + - Amazonia + - Central Africa + - Indonesia + - S America + - Brazil + - Sahel + - Southern Africa + - India + - Indochina + #- Sahara Desert + #- Arabian Peninsula + - Australia + #- Central Asia ## Was Broken... probably because there were two? + #- Mongolia + #- Tibetan Plateau + + +#END OF FILE diff --git a/env/ldf_v0.0.yaml b/env/ldf_v0.0.yaml new file mode 100644 index 000000000..26040ee7f --- /dev/null +++ b/env/ldf_v0.0.yaml @@ -0,0 +1,439 @@ +name: ldf_v0.0 +channels: + - conda-forge + - defaults + - r +dependencies: + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_gnu + - alsa-lib=1.2.13=hb9d3cd8_0 + - annotated-types=0.7.0=pyhd8ed1ab_0 + - antimeridian=0.3.11=pyhd8ed1ab_0 + - aom=3.9.1=hac33072_0 + - asciitree=0.3.3=py_2 + - asttokens=2.4.1=pyhd8ed1ab_0 + - aws-c-auth=0.8.0=hb88c0a9_10 + - aws-c-cal=0.8.0=hecf86a2_2 + - aws-c-common=0.10.3=hb9d3cd8_0 + - aws-c-compression=0.3.0=hf42f96a_2 + - aws-c-event-stream=0.5.0=h1ffe551_7 + - aws-c-http=0.9.1=hab05fe4_2 + - aws-c-io=0.15.2=hdeadb07_2 + - aws-c-mqtt=0.11.0=h7bd072d_8 + - aws-c-s3=0.7.1=h3a84f74_3 + - aws-c-sdkutils=0.2.1=hf42f96a_1 + - aws-checksums=0.2.2=hf42f96a_1 + - aws-crt-cpp=0.29.4=h21d7256_1 + - aws-sdk-cpp=1.11.449=h1a02111_2 + - azure-core-cpp=1.14.0=h5cfcd09_0 + - azure-identity-cpp=1.10.0=h113e628_0 + - azure-storage-blobs-cpp=12.13.0=h3cf044e_1 + - azure-storage-common-cpp=12.8.0=h736e048_1 + - azure-storage-files-datalake-cpp=12.12.0=ha633028_1 + - bleach=6.2.0=pyhd8ed1ab_0 + - blosc=1.21.6=hef167b5_0 + - bokeh=3.5.2=pyhd8ed1ab_0 + - bottleneck=1.4.2=py311h9f3472d_0 + - branca=0.7.2=pyhd8ed1ab_0 + - brotli=1.1.0=hb9d3cd8_2 + - brotli-bin=1.1.0=hb9d3cd8_2 + - brotli-python=1.1.0=py311hfdbb021_2 + - bzip2=1.0.8=h4bc722e_7 + - c-ares=1.34.3=heb4867d_0 + - ca-certificates=2025.1.31=hbcca054_0 + - cairo=1.18.0=hebfffa5_3 + - cartopy=0.24.0=py311h7db5c69_0 + - certifi=2025.1.31=pyhd8ed1ab_0 + - cf_xarray=0.10.0=pyhd8ed1ab_0 + - cffi=1.17.1=py311hf29c0ef_0 + - cftime=1.6.4=py311h9f3472d_1 + - charset-normalizer=3.4.0=pyhd8ed1ab_0 + - click=8.1.7=unix_pyh707e725_0 + - cloudpickle=3.1.0=pyhd8ed1ab_1 + - colorama=0.4.6=pyhd8ed1ab_0 + - colorcet=3.1.0=pyhd8ed1ab_0 + - comm=0.2.2=pyhd8ed1ab_0 + - contourpy=1.3.1=py311hd18a35c_0 + - cycler=0.12.1=pyhd8ed1ab_0 + - cyrus-sasl=2.1.27=h54b06d7_7 + - cytoolz=1.0.0=py311h9ecbd09_1 + - dask=2024.11.2=pyhff2d567_1 + - dask-core=2024.11.2=pyhff2d567_1 + - dask-expr=1.1.19=pyhd8ed1ab_0 + - dask-jobqueue=0.9.0=pyhd8ed1ab_0 + - datashader=0.16.3=pyhd8ed1ab_0 + - 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matplotlib-base=3.9.2=py311h2b939e6_2 + - matplotlib-inline=0.1.7=pyhd8ed1ab_0 + - mdit-py-plugins=0.4.2=pyhd8ed1ab_0 + - mdurl=0.1.2=pyhd8ed1ab_0 + - metpy=1.6.3=pyhd8ed1ab_0 + - minizip=4.0.7=h401b404_0 + - msgpack-python=1.1.0=py311hd18a35c_0 + - multipledispatch=0.6.0=pyhd8ed1ab_1 + - munkres=1.1.4=pyh9f0ad1d_0 + - mysql-common=9.0.1=h266115a_2 + - mysql-libs=9.0.1=he0572af_2 + - nc-time-axis=1.4.1=pyhd8ed1ab_0 + - ncar-jobqueue=2021.4.14=pyh44b312d_0 + - ncurses=6.5=he02047a_1 + - nest-asyncio=1.6.0=pyhd8ed1ab_0 + - netcdf-fortran=4.6.1=nompi_h22f9119_108 + - netcdf4=1.7.2=nompi_py311hae66bec_101 + - networkx=3.4.2=pyh267e887_2 + - numba=0.60.0=py311h4bc866e_0 + - numcodecs=0.14.0=py311h7db5c69_0 + - numpy=1.26.4=py311h64a7726_0 + - openjpeg=2.5.2=h488ebb8_0 + - openldap=2.6.8=hedd0468_0 + - openssl=3.4.1=h7b32b05_0 + - orc=2.0.3=he039a57_0 + - packaging=24.2=pyhff2d567_1 + - pandas=2.2.3=py311h7db5c69_1 + - panel=1.5.4=pyhd8ed1ab_0 + - param=2.1.1=pyhff2d567_0 + - parso=0.8.4=pyhd8ed1ab_0 + - partd=1.4.2=pyhd8ed1ab_0 + - patsy=1.0.1=pyhff2d567_0 + - pcre2=10.44=hba22ea6_2 + - pexpect=4.9.0=pyhd8ed1ab_0 + - pickleshare=0.7.5=py_1003 + - pillow=11.0.0=py311h49e9ac3_0 + - pint=0.24.4=pyhd8ed1ab_0 + - pip=24.3.1=pyh8b19718_0 + - pixman=0.43.2=h59595ed_0 + - platformdirs=4.3.6=pyhd8ed1ab_0 + - pooch=1.8.2=pyhd8ed1ab_0 + - proj=9.5.0=h12925eb_0 + - prompt-toolkit=3.0.48=pyha770c72_0 + - properscoring=0.1=py_0 + - psutil=6.1.0=py311h9ecbd09_0 + - pthread-stubs=0.4=hb9d3cd8_1002 + - ptyprocess=0.7.0=pyhd3deb0d_0 + - pure_eval=0.2.3=pyhd8ed1ab_0 + - pyarrow=18.0.0=py311h38be061_1 + - pyarrow-core=18.0.0=py311h4854187_1_cpu + - pycparser=2.22=pyhd8ed1ab_0 + - pyct=0.5.0=pyhd8ed1ab_0 + - pydantic=2.9.2=pyhd8ed1ab_0 + - pydantic-core=2.23.4=py311h9e33e62_0 + - pygments=2.18.0=pyhd8ed1ab_0 + - pyogrio=0.10.0=py311hf6089d3_1 + - pyparsing=3.2.0=pyhd8ed1ab_1 + - pyproj=3.7.0=py311h0f98d5a_0 + - pyshp=2.3.1=pyhd8ed1ab_0 + - pyside6=6.8.0.2=py311h9053184_0 + - pysocks=1.7.1=pyha2e5f31_6 + - python=3.11.4=hab00c5b_0_cpython + - python-dateutil=2.9.0.post0=pyhff2d567_0 + - python-tzdata=2024.2=pyhd8ed1ab_0 + - python_abi=3.11=5_cp311 + - pytz=2024.1=pyhd8ed1ab_0 + - pyviz_comms=3.0.3=pyhd8ed1ab_0 + - pyyaml=6.0.2=py311h9ecbd09_1 + - pyzmq=26.2.0=py311h7deb3e3_3 + - qhull=2020.2=h434a139_5 + - qt6-main=6.8.0=h6e8976b_0 + - rav1e=0.6.6=he8a937b_2 + - re2=2024.07.02=h77b4e00_1 + - readline=8.2=h8228510_1 + - requests=2.32.3=pyhd8ed1ab_0 + - retrying=1.3.3=pyhd8ed1ab_3 + - s2n=1.5.9=h0fd0ee4_0 + - scikit-learn=1.5.2=py311h57cc02b_1 + - scipy=1.14.1=py311he9a78e4_1 + - setuptools=75.5.0=pyhff2d567_0 + - shapely=2.0.6=py311h2fdb869_2 + - six=1.16.0=pyh6c4a22f_0 + - snappy=1.2.1=ha2e4443_0 + - sortedcontainers=2.4.0=pyhd8ed1ab_0 + - sparse=0.15.5=pyh72ffeb9_0 + - spatialpandas=0.4.10=pyhd8ed1ab_1 + - sqlite=3.47.0=h9eae976_1 + - stack_data=0.6.2=pyhd8ed1ab_0 + - statsmodels=0.14.4=py311h9f3472d_0 + - svt-av1=2.3.0=h5888daf_0 + - tblib=3.0.0=pyhd8ed1ab_0 + - threadpoolctl=3.5.0=pyhc1e730c_0 + - tk=8.6.13=noxft_h4845f30_101 + - toolz=1.0.0=pyhd8ed1ab_0 + - tornado=6.4.1=py311h9ecbd09_1 + - tqdm=4.67.0=pyhd8ed1ab_0 + - traitlets=5.14.3=pyhd8ed1ab_0 + - typing-extensions=4.12.2=hd8ed1ab_0 + - typing_extensions=4.12.2=pyha770c72_0 + - tzdata=2024b=hc8b5060_0 + - uc-micro-py=1.0.3=pyhd8ed1ab_0 + - unicodedata2=15.1.0=py311h9ecbd09_1 + - uriparser=0.9.8=hac33072_0 + - urllib3=2.2.3=pyhd8ed1ab_0 + - uxarray=2025.3.0 + - wayland=1.23.1=h3e06ad9_0 + - wcwidth=0.2.13=pyhd8ed1ab_0 + - webencodings=0.5.1=pyhd8ed1ab_2 + - wheel=0.45.0=pyhd8ed1ab_0 + - x265=3.5=h924138e_3 + - xarray=2024.10.0=pyhd8ed1ab_0 + - xcb-util=0.4.1=hb711507_2 + - xcb-util-cursor=0.1.5=hb9d3cd8_0 + - xcb-util-image=0.4.0=hb711507_2 + - xcb-util-keysyms=0.4.1=hb711507_0 + - xcb-util-renderutil=0.3.10=hb711507_0 + - xcb-util-wm=0.4.2=hb711507_0 + - xerces-c=3.2.5=h988505b_2 + - xesmf=0.8.8=pyhd8ed1ab_1 + - xhistogram=0.3.2=pyhd8ed1ab_0 + - xkeyboard-config=2.43=hb9d3cd8_0 + - xorg-libice=1.1.1=hb9d3cd8_1 + - xorg-libsm=1.2.4=he73a12e_1 + - xorg-libx11=1.8.10=h4f16b4b_0 + - xorg-libxau=1.0.11=hb9d3cd8_1 + - xorg-libxcomposite=0.4.6=hb9d3cd8_2 + - xorg-libxcursor=1.2.3=hb9d3cd8_0 + - xorg-libxdamage=1.1.6=hb9d3cd8_0 + - xorg-libxdmcp=1.1.5=hb9d3cd8_0 + - xorg-libxext=1.3.6=hb9d3cd8_0 + - xorg-libxfixes=6.0.1=hb9d3cd8_0 + - xorg-libxi=1.8.2=hb9d3cd8_0 + - xorg-libxrandr=1.5.4=hb9d3cd8_0 + - xorg-libxrender=0.9.11=hb9d3cd8_1 + - xorg-libxtst=1.2.5=hb9d3cd8_3 + - xorg-libxxf86vm=1.1.5=hb9d3cd8_4 + - xorg-xorgproto=2024.1=hb9d3cd8_1 + - xskillscore=0.0.26=pyhd8ed1ab_0 + - xyzservices=2024.9.0=pyhd8ed1ab_0 + - xz=5.2.6=h166bdaf_0 + - yaml=0.2.5=h7f98852_2 + - zarr=2.18.3=pyhd8ed1ab_0 + - zeromq=4.3.5=h3b0a872_7 + - zict=3.0.0=pyhd8ed1ab_0 + - zipp=3.21.0=pyhd8ed1ab_0 + - zlib=1.3.1=hb9d3cd8_2 + - zstandard=0.23.0=py311hbc35293_1 + - zstd=1.5.6=ha6fb4c9_0 + - pip: + - alabaster==1.0.0 + - anyio==4.6.2.post1 + - argon2-cffi==23.1.0 + - argon2-cffi-bindings==21.2.0 + - arrow==1.3.0 + - async-lru==2.0.4 + - attrs==24.2.0 + - babel==2.16.0 + - beautifulsoup4==4.12.3 + - cmocean==4.0.3 + - defusedxml==0.7.1 + - docutils==0.21.2 + - fastjsonschema==2.20.0 + - fqdn==1.5.1 + - h11==0.14.0 + - httpcore==1.0.7 + - httpx==0.27.2 + - imagesize==1.4.1 + - iniconfig==2.0.0 + - ipywidgets==8.1.5 + - isoduration==20.11.0 + - json5==0.9.28 + - jsonpointer==3.0.0 + - jsonschema==4.23.0 + - jsonschema-specifications==2024.10.1 + - jupyter==1.1.1 + - jupyter-console==6.6.3 + - jupyter-events==0.10.0 + - jupyter-lsp==2.2.5 + - jupyter-server==2.14.2 + - jupyter-server-terminals==0.5.3 + - jupyterlab==4.2.6 + - jupyterlab-pygments==0.3.0 + - jupyterlab-server==2.27.3 + - jupyterlab-widgets==3.0.13 + - mistune==3.0.2 + - nbclient==0.10.0 + - nbconvert==7.16.4 + - nbformat==5.10.4 + - nbsphinx==0.9.5 + - notebook==7.2.2 + - notebook-shim==0.2.4 + - overrides==7.7.0 + - pandocfilters==1.5.1 + - pluggy==1.5.0 + - prometheus-client==0.21.0 + - pytest==8.3.3 + - python-json-logger==2.0.7 + - referencing==0.35.1 + - rfc3339-validator==0.1.4 + - rfc3986-validator==0.1.1 + - rpds-py==0.21.0 + - send2trash==1.8.3 + - sniffio==1.3.1 + - snowballstemmer==2.2.0 + - soupsieve==2.6 + - sphinx==8.1.3 + - sphinxcontrib-applehelp==2.0.0 + - sphinxcontrib-devhelp==2.0.0 + - sphinxcontrib-htmlhelp==2.1.0 + - sphinxcontrib-jsmath==1.0.1 + - sphinxcontrib-qthelp==2.0.0 + - sphinxcontrib-serializinghtml==2.0.0 + - terminado==0.18.1 + - tinycss2==1.4.0 + - types-python-dateutil==2.9.0.20241003 + - uri-template==1.3.0 + - webcolors==24.11.1 + - websocket-client==1.8.0 + - widgetsnbextension==4.0.13 +prefix: /glade/work/wwieder/conda-envs/cupid-analysis-justin diff --git a/lib/adf_dataset.py b/lib/adf_dataset.py index eb89dfaf2..18544b7a4 100644 --- a/lib/adf_dataset.py +++ b/lib/adf_dataset.py @@ -1,12 +1,9 @@ from pathlib import Path import xarray as xr - +import uxarray as ux +#import adf_utils as utils import warnings # use to warn user about missing files - -def my_formatwarning(msg, *args, **kwargs): - # ignore everything except the message - return str(msg) + '\n' -warnings.formatwarning = my_formatwarning +#warnings.formatwarning = utils.my_formatwarning # "reference data" # It is often just a "baseline case", @@ -64,11 +61,13 @@ def __init__(self, adfobj): def set_reference(self): """Set attributes for reference (aka baseline) data location, names, and variables.""" if self.adf.compare_obs: - self.ref_var_loc = {v: self.adf.var_obs_dict[v]['obs_file'] for v in self.adf.var_obs_dict} - self.ref_labels = {v: self.adf.var_obs_dict[v]['obs_name'] for v in self.adf.var_obs_dict} - self.ref_var_nam = {v: self.adf.var_obs_dict[v]['obs_var'] for v in self.adf.var_obs_dict} - self.ref_case_label = "Obs" - if not self.adf.var_obs_dict: + if "var_obs_dict" in dir(self.adf): + self.ref_var_loc = {v: self.adf.var_obs_dict[v]['obs_file'] for v in self.adf.var_obs_dict} + self.ref_labels = {v: self.adf.var_obs_dict[v]['obs_name'] for v in self.adf.var_obs_dict} + self.ref_var_nam = {v: self.adf.var_obs_dict[v]['obs_var'] for v in self.adf.var_obs_dict} + self.ref_case_label = "Obs" + else: + #if not self.adf.var_obs_dict: warnings.warn("\t WARNING: reference is observations, but no observations found to plot against.") else: self.ref_var_loc = {} @@ -101,6 +100,24 @@ def get_timeseries_file(self, case, field): ts_filenames = f'{case}.*.{field}.*nc' ts_files = sorted(ts_loc.glob(ts_filenames)) return ts_files + + def load_timeseries_dataset(self, case, field, **kwargs): + """Return a data set for variable field.""" + fils = self.get_timeseries_file(case, field) + if not fils: + warnings.warn(f"\t WARNING: Did not find time series file(s) for case: {case}, variable: {field}") + return None + return self.load_dataset(fils, type="tseries", **kwargs) + + def load_timeseries_da(self, case, variablename, **kwargs): + """Return DataArray from time series file(s). + Uses defaults file to convert units. + """ + fils = self.get_timeseries_file(case, variablename) + if not fils: + warnings.warn(f"\t WARNING: Did not find case time series file(s), variable: {variablename}") + return None + return self.load_da(fils, variablename, case, **kwargs) # Reference case (baseline/obs) def get_ref_timeseries_file(self, field): @@ -113,65 +130,26 @@ def get_ref_timeseries_file(self, field): ts_filenames = f'{self.ref_case_label}.*.{field}.*nc' ts_files = sorted(ts_loc.glob(ts_filenames)) return ts_files - - - def load_timeseries_dataset(self, fils): - """Return DataSet from time series file(s) and assign time to midpoint of interval""" - if (len(fils) == 0): - warnings.warn("\t WARNING: Input file list is empty.") - return None - elif (len(fils) > 1): - ds = xr.open_mfdataset(fils, decode_times=False) - else: - sfil = str(fils[0]) - if not Path(sfil).is_file(): - warnings.warn(f"\t WARNING: Expecting to find file: {sfil}") - return None - ds = xr.open_dataset(sfil, decode_times=False) - if ds is None: - warnings.warn(f"\t WARNING: invalid data on load_dataset") - # assign time to midpoint of interval (even if it is already) - if 'time_bnds' in ds: - t = ds['time_bnds'].mean(dim='nbnd') - t.attrs = ds['time'].attrs - ds = ds.assign_coords({'time':t}) - elif 'time_bounds' in ds: - t = ds['time_bounds'].mean(dim='nbnd') - t.attrs = ds['time'].attrs - ds = ds.assign_coords({'time':t}) - else: - warnings.warn("\t INFO: Timeseries file does not have time bounds info.") - return xr.decode_cf(ds) - - def load_timeseries_da(self, case, variablename): - """Return DataArray from time series file(s). - Uses defaults file to convert units. - """ - add_offset, scale_factor = self.get_value_converters(case, variablename) - fils = self.get_timeseries_file(case, variablename) + + def load_reference_timeseries_dataset(self, field, **kwargs): + """Return a data set for variable field.""" + case = self.ref_case_label + fils = self.get_ref_timeseries_file(field) if not fils: - warnings.warn(f"\t WARNING: Did not find case time series file(s), variable: {variablename}") + warnings.warn(f"\t WARNING: Did not find time series file(s) for case: {case}, variable: {field}") return None - return self.load_da(fils, variablename, add_offset=add_offset, scale_factor=scale_factor) + return self.load_dataset(fils, type="tseries", **kwargs) - def load_reference_timeseries_da(self, field): + def load_reference_timeseries_da(self, field, **kwargs): """Return a DataArray time series to be used as reference (aka baseline) for variable field. """ + case = self.ref_case_label fils = self.get_ref_timeseries_file(field) if not fils: warnings.warn(f"\t WARNING: Did not find reference time series file(s), variable: {field}") return None - #Change the variable name from CAM standard to what is - # listed in variable defaults for this observation field - if self.adf.compare_obs: - field = self.ref_var_nam[field] - add_offset = 0 - scale_factor = 1 - else: - add_offset, scale_factor = self.get_value_converters(self.ref_case_label, field) - - return self.load_da(fils, field, add_offset=add_offset, scale_factor=scale_factor) + return self.load_da(fils, field, case, **kwargs) #------------------ @@ -181,48 +159,62 @@ def load_reference_timeseries_da(self, field): #------------------ # Test case(s) - def load_climo_da(self, case, variablename): - """Return DataArray from climo file""" - add_offset, scale_factor = self.get_value_converters(case, variablename) - fils = self.get_climo_file(case, variablename) - return self.load_da(fils, variablename, add_offset=add_offset, scale_factor=scale_factor) - - - def load_climo_file(self, case, variablename): - """Return Dataset for climo of variablename""" - fils = self.get_climo_file(case, variablename) - if not fils: - warnings.warn(f"\t WARNING: Did not find climo file for variable: {variablename}. Will try to skip.") - return None - return self.load_dataset(fils) - - def get_climo_file(self, case, variablename): """Retrieve the climo file path(s) for variablename for a specific case.""" a = self.adf.get_cam_info("cam_climo_loc", required=True) # list of paths (could be multiple cases) caseindex = (self.case_names).index(case) # the entry for specified case model_cl_loc = Path(a[caseindex]) return sorted(model_cl_loc.glob(f"{case}_{variablename}_climo.nc")) + + def load_climo_dataset(self, case, variablename, **kwargs): + """Return Dataset for climo of field""" + fils = self.get_climo_file(case, variablename) + if not fils: + warnings.warn(f"\t WARNING: Did not find climo file(s) for case: {case}, variable: {variablename}") + return None + return self.load_dataset(fils, **kwargs) + + def load_climo_da(self, case, variablename, **kwargs): + """Return DataArray from climo file""" + fils = self.get_climo_file(case, variablename) + if not fils: + warnings.warn(f"\t WARNING: Did not find climo file(s) for case: {case}, variable: {variablename}") + return None + return self.load_da(fils, variablename, case, **kwargs) # Reference case (baseline/obs) - def load_reference_climo_da(self, case, variablename): - """Return DataArray from reference (aka baseline) climo file""" - add_offset, scale_factor = self.get_value_converters(case, variablename) - fils = self.get_reference_climo_file(variablename) - return self.load_da(fils, variablename, add_offset=add_offset, scale_factor=scale_factor) - def get_reference_climo_file(self, var): """Return a list of files to be used as reference (aka baseline) for variable var.""" if self.adf.compare_obs: fils = self.ref_var_loc.get(var, None) return [fils] if fils is not None else None ref_loc = self.adf.get_baseline_info("cam_climo_loc") + if not ref_loc: + return None # NOTE: originally had this looking for *_baseline.nc fils = sorted(Path(ref_loc).glob(f"{self.ref_case_label}_{var}_climo.nc")) if fils: return fils return None + + def load_reference_climo_dataset(self, field, **kwargs): + """Return Dataset for climo of field""" + case = self.ref_case_label + fils = self.get_reference_climo_file(field) + if not fils: + warnings.warn(f"\t WARNING: Did not find climo file(s) for case: {case}, variable: {field}") + return None + return self.load_dataset(fils, **kwargs) + + def load_reference_climo_da(self, field, **kwargs): + """Return DataArray from reference (aka baseline) climo file""" + case = self.ref_case_label + fils = self.get_reference_climo_file(field) + if not fils: + warnings.warn(f"\t WARNING: Did not find climo file(s) for case: {case}, variable: {field}") + return None + return self.load_da(fils, field, case, **kwargs) #------------------ @@ -238,28 +230,28 @@ def get_regrid_file(self, case, field): return sorted(model_rg_loc.glob(f"{rlbl}_{case}_{field}_regridded.nc")) - def load_regrid_dataset(self, case, field): + def load_regrid_dataset(self, case, field, **kwargs): """Return a data set to be used as reference (aka baseline) for variable field.""" fils = self.get_regrid_file(case, field) if not fils: warnings.warn(f"\t WARNING: Did not find regrid file(s) for case: {case}, variable: {field}") return None - return self.load_dataset(fils) + return self.load_dataset(fils, **kwargs) - def load_regrid_da(self, case, field): + def load_regrid_da(self, case, field, **kwargs): """Return a data array to be used as reference (aka baseline) for variable field.""" - add_offset, scale_factor = self.get_value_converters(case, field) fils = self.get_regrid_file(case, field) if not fils: warnings.warn(f"\t WARNING: Did not find regrid file(s) for case: {case}, variable: {field}") return None - return self.load_da(fils, field, add_offset=add_offset, scale_factor=scale_factor) + return self.load_da(fils, field, case, **kwargs) # Reference case (baseline/obs) - def get_ref_regrid_file(self, case, field): + def get_ref_regrid_file(self, field): """Return list of reference regridded files""" + case = self.ref_case_label if self.adf.compare_obs: obs_loc = self.ref_var_loc.get(field, None) if obs_loc: @@ -272,19 +264,20 @@ def get_ref_regrid_file(self, case, field): return fils - def load_reference_regrid_dataset(self, case, field): + def load_reference_regrid_dataset(self, field, **kwargs): """Return a data set to be used as reference (aka baseline) for variable field.""" - fils = self.get_ref_regrid_file(case, field) + case = self.ref_case_label + fils = self.get_ref_regrid_file(field) if not fils: warnings.warn(f"\t WARNING: Did not find regridded file(s) for case: {case}, variable: {field}") return None - return self.load_dataset(fils) + return self.load_dataset(fils, **kwargs) - def load_reference_regrid_da(self, case, field): + def load_reference_regrid_da(self, field, **kwargs): """Return a data array to be used as reference (aka baseline) for variable field.""" - add_offset, scale_factor = self.get_value_converters(case, field) - fils = self.get_ref_regrid_file(case, field) + case = self.ref_case_label + fils = self.get_ref_regrid_file(field) if not fils: warnings.warn(f"\t WARNING: Did not find regridded file(s) for case: {case}, variable: {field}") return None @@ -292,7 +285,7 @@ def load_reference_regrid_da(self, case, field): # listed in variable defaults for this observation field if self.adf.compare_obs: field = self.ref_var_nam[field] - return self.load_da(fils, field, add_offset=add_offset, scale_factor=scale_factor) + return self.load_da(fils, field, case, **kwargs) #------------------ @@ -301,8 +294,10 @@ def load_reference_regrid_da(self, case, field): #--------------------------- # Load DataSet - def load_dataset(self, fils): + def load_dataset(self, fils, type=None, **kwargs): """Return xarray DataSet from file(s)""" + unstructured_plotting = kwargs.get("unstructured_plotting",False) + if (len(fils) == 0): warnings.warn("\t WARNING: Input file list is empty.") return None @@ -313,27 +308,63 @@ def load_dataset(self, fils): if not Path(sfil).is_file(): warnings.warn(f"\t WARNING: Expecting to find file: {sfil}") return None - ds = xr.open_dataset(sfil) + # Open unstructured data if requested + if unstructured_plotting: + if "mesh_file" not in kwargs: + msg = "\t WARNING: Unstructured plotting is requested, but no available mesh file." + msg += " Please make sure 'mesh_file' is declared in 'diag_basic_info' in config file" + print(msg) + ds = None + mesh = kwargs["mesh_file"] + ds = ux.open_dataset(mesh, sfil) + else: + ds = xr.open_dataset(sfil) if ds is None: warnings.warn(f"\t WARNING: invalid data on load_dataset") + if type == "tseries": + # assign time to midpoint of interval (even if it is already) + if 'time_bnds' in ds: + t = ds['time_bnds'].mean(dim='nbnd') + t.attrs = ds['time'].attrs + ds = ds.assign_coords({'time':t}) + # TODO check this for old land model output + elif 'hist_interval' in ds['time_bounds'].dims: + t = ds['time_bounds'].mean(dim='hist_interval')#.value + t.attrs = ds['time'].attrs + ds = ds.assign_coords({'time':t}) + elif 'time_bounds' in ds: + t = ds['time_bounds'].mean(dim='nbnd') + t.attrs = ds['time'].attrs + ds = ds.assign_coords({'time':t}) + + + else: + warnings.warn("\t INFO: dataset does not have time bounds info.") return ds # Load DataArray - def load_da(self, fils, variablename, **kwargs): + def load_da(self, fils, variablename, case, type=None, **kwargs): """Return xarray DataArray from files(s) w/ optional scale factor, offset, and/or new units""" - ds = self.load_dataset(fils) + ds = self.load_dataset(fils, **kwargs) if ds is None: warnings.warn(f"\t WARNING: Load failed for {variablename}") return None da = (ds[variablename]).squeeze() - scale_factor = kwargs.get('scale_factor', 1) - add_offset = kwargs.get('add_offset', 0) + units = da.attrs.get('units', '--') + add_offset, scale_factor = self.get_value_converters(case, variablename) da = da * scale_factor + add_offset + da.attrs['scale_factor'] = scale_factor + da.attrs['add_offset'] = add_offset + da = self.update_unit(variablename, da, units) + da.attrs['original_unit'] = units + return da + + def update_unit(self, variablename, da, units): if variablename in self.adf.variable_defaults: vres = self.adf.variable_defaults[variablename] - da.attrs['units'] = vres.get("new_unit", da.attrs.get('units', 'none')) + da.attrs['units'] = vres.get("new_unit", units) else: - da.attrs['units'] = 'none' + da.attrs['units'] = '--' return da # Get variable conversion defaults, if applicable diff --git a/lib/adf_diag.py b/lib/adf_diag.py index cb611376f..2f6520b18 100644 --- a/lib/adf_diag.py +++ b/lib/adf_diag.py @@ -26,6 +26,8 @@ from pathlib import Path from typing import Optional +import utils as adf_utils + # Check if "PyYAML" is present in python path: # pylint: disable=unused-import try: @@ -67,6 +69,30 @@ print("Please install module, e.g. 'pip install Cartopy'.") sys.exit(1) +# Check if "uxarray" is present in python path: +#try: +# import uxarray as ux +#except ImportError: +# print("uxarray module does not exist in python path.") +# print("Please install module, e.g. 'pip install uxarray'.") +# sys.exit(1) + +# Check if "esmpy" is present in python path: +try: + import esmpy +except ImportError: + print("xesmf module does not exist in python path.") + print("Please install module, e.g. 'pip install esmpy'.") + sys.exit(1) + +# Check if "xesmf" is present in python path: +try: + import xesmf +except ImportError: + print("xesmf module does not exist in python path.") + print("Please install module, e.g. 'pip install xesmf'.") + sys.exit(1) + # pylint: enable=unused-import # +++++++++++++++++++++++++++++ @@ -358,6 +384,9 @@ def call_ncrcat(cmd): case_type_string = "baseline" hist_str_list = [self.hist_string["base_hist_str"]] + overwrite_regrid_locs = [self.get_baseline_info("cam_overwrite_ts_regrid")] + test_output_loc = [self.get_baseline_info("cam_ts_regrid_loc")] + else: # Use test case settings, which are already lists: case_names = self.get_cam_info("cam_case_name", required=True) @@ -367,8 +396,11 @@ def call_ncrcat(cmd): overwrite_ts = self.get_cam_info("cam_overwrite_ts") start_years = self.climo_yrs["syears"] end_years = self.climo_yrs["eyears"] - case_type_string="case" + case_type_string = "test" hist_str_list = self.hist_string["test_hist_str"] + + overwrite_regrid_locs = self.get_cam_info("cam_overwrite_ts_regrid") + test_output_loc = self.get_cam_info("cam_ts_regrid_loc") # End if # Read hist_str (component.hist_num) from the yaml file, or set to default @@ -378,11 +410,13 @@ def call_ncrcat(cmd): # get info about variable defaults res = self.variable_defaults + comp = self.model_component + # Loop over cases: for case_idx, case_name in enumerate(case_names): # Check if particular case should be processed: if cam_ts_done[case_idx]: - emsg = "\tNOTE: Configuration file indicates time series files have been pre-computed" + emsg = "\tNOTE: Config. file indicates time series files have been pre-computed" emsg += f" for case '{case_name}'. Will rely on those files directly." print(emsg) continue @@ -395,9 +429,11 @@ def call_ncrcat(cmd): # Create path object for the CAM history file(s) location: starting_location = Path(cam_hist_locs[case_idx]) + #unstruct = unstructed[case_idx] + # Check that path actually exists: if not starting_location.is_dir(): - emsg = f"Provided {case_type_string} 'cam_hist_loc' directory" + emsg = f"Provided {case_type_string} case 'cam_hist_loc' directory" emsg += f" '{starting_location}' not found. Script is ending here." self.end_diag_fail(emsg) # End if @@ -409,7 +445,7 @@ def call_ncrcat(cmd): hist_str_case = hist_str_list[case_idx] for hist_str in hist_str_case: - print(f"\t Processing time series for {case_type_string} {case_name}, {hist_str} files:") + print(f"\t Processing time series for {case_type_string} case '{case_name}', {hist_str} files:") if not list(starting_location.glob("*" + hist_str + ".*.nc")): emsg = ( f"No history *{hist_str}.*.nc files found in '{starting_location}'." @@ -641,8 +677,9 @@ def call_ncrcat(cmd): # Lastly, raise error if the variable is not a derived quanitity # but is also not in the history file(s) else: - msg = f"\t WARNING: {var} is not in the history file for case '{case_name}' " - msg += "nor can it be derived. Script will continue to next variable." + msg = f"\t WARNING: {var} is not in the history file for case " + msg += "'{case_name}' nor can it be derived. Script will continue " + msg += " to the text variable." print(msg) logmsg = f"create time series for {case_name}:" logmsg += f"\n {var} is not in the file {hist_files[0]} " @@ -652,7 +689,8 @@ def call_ncrcat(cmd): # End if (var in var_diag_list) # Check if variable has a "lev" dimension according to first file: - has_lev = bool("lev" in hist_file_ds[var].dims or "ilev" in hist_file_ds[var].dims) + has_lev = bool("lev" in hist_file_ds[var].dims or \ + "ilev" in hist_file_ds[var].dims) # Check if files already exist in time series directory: ts_file_list = glob.glob(ts_outfil_str) @@ -706,15 +744,22 @@ def call_ncrcat(cmd): # End if cam # End if has_lev + # Optional, add additional time varying variables to clm2.h1 files + if ("clm" in hist_str) and ("h1" in hist_str): + ncrcat_var_list = ncrcat_var_list + ",pfts1d_wtgcell,pfts1d_wtlunit,pfts1d_wtcol" + cmd = ( ["ncrcat", "-O", "-4", "-h", "--no_cll_mth", "-v", ncrcat_var_list] + hist_files + ["-o", ts_outfil_str] ) - # Example ncatted command (you can modify it with the specific attribute changes you need) - #cmd_ncatted = ["ncatted", "-O", "-a", f"adf_user,global,a,c,{self.user}", ts_outfil_str] - # Step 1: Convert Path objects to strings and concatenate the list of historical files into a single string + # Example ncatted command + # (you can modify it with the specific attribute changes you need) + # cmd_ncatted = ["ncatted", "-O", "-a", "f" adf_user,global,a,c,{self.user}", + # ts_outfil_str] + # Step 1: Convert Path objects to strings and concatenate the list of + # historical files into a single string hist_files_str = ', '.join(str(f.name) for f in hist_files) hist_locs_str = ', '.join(str(loc) for loc in cam_hist_locs) @@ -723,25 +768,28 @@ def call_ncrcat(cmd): "ncatted", "-O", "-a", "adf_user,global,a,c," + f"{self.user}", "-a", "hist_file_locs,global,a,c," + f"{hist_locs_str}", - "-a", "hist_file_list,global,a,c," + f"{hist_files_str}", + # This list is too long and fails + # "-a", "hist_file_list,global,a,c," + f"{hist_files_str}", ts_outfil_str ] - # Step 3: Create the ncatted command to remove the history attribute + # Step 3c: Create the ncatted command to remove the history attribute cmd_remove_history = [ "ncatted", "-O", "-h", "-a", "history,global,d,,", ts_outfil_str ] + # Add to command list for use in multi-processing pool: # ----------------------------------------------------- # generate time series files list_of_commands.append(cmd) # Add global attributes: user, original hist file loc(s) and all filenames list_of_ncattend_commands.append(cmd_ncatted) + # Remove the `history` attr that gets tacked on (for clean up) - # NOTE: this may not be best practice, but it the history attr repeats + # NOTE: this may not be best practice, but if the history attr repeats # the files attrs so the global attrs become obtrusive... list_of_hist_commands.append(cmd_remove_history) @@ -756,16 +804,117 @@ def call_ncrcat(cmd): with mp.Pool(processes=self.num_procs) as mpool: _ = mpool.map(call_ncrcat, list_of_ncattend_commands) - # Run ncatted command to remove history attribute after the global attributes are set + # Run ncatted command to remove history attribute after + # the global attributes are set with mp.Pool(processes=self.num_procs) as mpool: _ = mpool.map(call_ncrcat, list_of_hist_commands) + # Loop over the created time series files again and fix the time if necessary + #NOTE: There is no solution to do this with NCO operators, + # but there is with CDO operators. We can switch to using CDO, + # but it would require the user to have/load CDO as well. + fils = glob.glob(f"{ts_dir}/*{time_string}.nc") + for fil in fils: + ts_ds = xr.open_dataset(fil, decode_times=False) + if ('time_bnds' in ts_ds) or ('time_bounds' in ts_ds): + if comp == "atm": + if 'time_bnds' in ts_ds: + ts_ds.time_bnds.attrs['units'] = ts_ds.time.attrs['units'] + ts_ds.time_bnds.attrs['calendar'] = ts_ds.time.attrs['calendar'] + if 'time_bounds' in ts_ds: + ts_ds.time_bounds.attrs['units'] = ts_ds.time.attrs['units'] + ts_ds.time_bounds.attrs['calendar'] = ts_ds.time.attrs['calendar'] + if comp == "lnd": + ts_ds.time_bounds.attrs['units'] = ts_ds.time.attrs['units'] + ts_ds.time_bounds.attrs['calendar'] = ts_ds.time.attrs['calendar'] + time = ts_ds['time'] + + if comp == "atm": + if 'time_bnds' in ts_ds: + time = xr.DataArray(ts_ds['time_bnds'].load().mean(dim='nbnd').values, + dims=time.dims, attrs=time.attrs) + if 'time_bounds' in ts_ds: + time = xr.DataArray(ts_ds['time_bounds'].load().mean(dim='nbnd').values, + dims=time.dims, attrs=time.attrs) + if comp == "lnd": + # need greater flexibility given changes in clm history files over time + if 'hist_interval' in ts_ds['time_bounds'].dims: + time = xr.DataArray(ts_ds['time_bounds'].load().mean(dim='hist_interval').values, + dims=time.dims, attrs=time.attrs) + else: + time = xr.DataArray(ts_ds['time_bounds'].load().mean(dim='nbnd').values, + dims=time.dims, attrs=time.attrs) + + # Optional, add additional variables to clm2.h0 files + if "h0" in hist_str: + ds = xr.open_dataset(hist_files[0], decode_times=False) + ts_ds['area'] = ds.area + ts_ds['landfrac'] = ds.landfrac + ts_ds['landmask'] = ds.landmask + # Optional, add additional variables to clm2.h1 files + # Note: this is currently set up for PFT output + if "h1" in hist_str: + ds = xr.open_dataset(hist_files[0], decode_times=False) + ts_ds['pfts1d_ixy'] = ds.pfts1d_ixy + ts_ds['pfts1d_jxy'] = ds.pfts1d_jxy + ts_ds['pfts1d_gi'] = ds.pfts1d_gi + ts_ds['pfts1d_li'] = ds.pfts1d_li + ts_ds['pfts1d_ci'] = ds.pfts1d_li + ts_ds['pfts1d_itype_veg'] = ds.pfts1d_itype_veg + ts_ds['pfts1d_itype_col'] = ds.pfts1d_itype_col + ts_ds['pfts1d_itype_lunit'] = ds.pfts1d_itype_lunit + ts_ds['pfts1d_active'] = ds.pfts1d_active + ts_ds['time'] = time + ts_ds.assign_coords(time=time) + ts_ds_fixed = xr.decode_cf(ts_ds) + + # Add attribute note of time change + attrs_dict = { + "adf_timeseries_info": "Time series files have been computed using ncrcat'", + "adf_note": "The time values have been modified to middle of month" + } + ts_ds_fixed = ts_ds_fixed.assign_attrs(attrs_dict) + + # Save to a temporary file + temp_file_path = fil + ".tmp" + ts_ds_fixed.to_netcdf(temp_file_path) + # Replace the original file with the modified file + os.replace(temp_file_path, fil) + if vars_to_derive: self.derive_variables( res=res, hist_str=hist_str, vars_to_derive=vars_to_derive, constit_dict=constit_dict, ts_dir=ts_dir ) # End with + + # DOES NOT WORK CORRECTLY! + grid_ts = False + if grid_ts: + # TEMPORARY: do a quick check if this on native grid and regrid + ts_0 = sorted(Path(ts_dir).glob("*.nc"))[0] + ts_file_ds = xr.open_dataset(ts_0) + + if adf_utils.check_unstructured(ts_file_ds, case_name): + print() + latlon_file = self.latlon_files[f"{case_type_string}_latlon_file"] + print("latlon_file",latlon_file,"\n") + #latlon_file = ts_0 + time_file = ts_file_ds + wgts_file = self.latlon_wgt_files[f"{case_type_string}_wgts_file"] + method = self.latlon_regrid_method[f"{case_type_string}_regrid_method"] + if not baseline: + wgts_file = wgts_file[case_idx] + method = method[case_idx] + latlon_file = latlon_file[case_idx] + + kwargs = {"ts_dir":ts_dir, "latlon_file":latlon_file, "wgts_file":wgts_file, + "method":method, "diag_var_list":self.diag_var_list, + "case_name":case_name, "hist_str":hist_str, + "time_string":time_string, "comp":comp,"time_file":time_file + } + adf_utils.grid_timeseries(**kwargs) + # End for hist_str # End cases loop @@ -1179,7 +1328,8 @@ def derive_variables(self, res=None, hist_str=None, vars_to_derive=None, ts_dir= # Check if all the necessary constituent files were found if len(constit_files) != len(constit_list): - ermsg = f"\t WARNING: Not all constituent files present; {var} cannot be calculated." + ermsg = f"\t WARNING: Not all constituent files present;" + ermsg += f" {var} cannot be calculated." ermsg += f" Please remove {var} from 'diag_var_list' or find the " ermsg += "relevant CAM files.\n" print(ermsg) @@ -1208,6 +1358,10 @@ def derive_variables(self, res=None, hist_str=None, vars_to_derive=None, ts_dir= # create new file name for derived variable derived_file = constit_files[0].replace(constit_list[0], var) + clmPDB = 0.0112372 # ratio of 13C/12C in Pee Dee Belemnite (C isotope standard) + clm14C = 1e-12 # not accepted value of 1.176 x 10-12 + min13C = -40. # prevent wacky values when 12C stock or fluxes are very small + min14C = -400. # arbitrary # Check if clobber is true for file if Path(derived_file).is_file(): if overwrite: @@ -1221,6 +1375,55 @@ def derive_variables(self, res=None, hist_str=None, vars_to_derive=None, ts_dir= # NOTE: this will need to be changed when derived equations are more complex! - JR if var == "RESTOM": der_val = ds["FSNT"]-ds["FLNT"] + elif var == "ASA": + der_val = 100*ds["FSR"]/ds["FSDS"].where(ds["FSDS"]>0) + elif var == "RNET": + der_val = ds["FSA"]-ds["FIRA"] + elif var == "WUE": + der_val = ds["GPP"]/ds["FCTR"].where(ds["FCTR"]>0) + + # ---------------------------------------------------------------------------------- + # Isotope-specific derived variables + # ---------------------------------------------------------------------------------- + # NOTE: del13C : valid range = -40 to 0 per mil PDB + # formulas similar to Jain et al 1996 Tellus B 48B: 583-600 + # as applied in land_diags /glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/code/shared/lnd_func.ncl + # TODO, this would be nice to avoid repeating for all isotopes enables variables + # TODO, check for accuracy of equations, neither as as in Jain et al 1996... + # Should they just be ((ratio sample - ratio standard) / ratio standard) * 1000 ? + + elif var == "C13_GPP_pm": + der_val = (((ds["C13_GPP"]/ds["GPP"].where(ds["GPP"]>0)) / clmPDB) - 1.) * 1e3 + der_val = der_val.where(der_val > min13C) + + elif var == "C14_GPP_pm": + der_val = (((ds["C14_GPP"]/ds["GPP"].where(ds["GPP"]>0)) / clm14C) - 1.) * 1e3 + der_val = der_val.where(der_val > min14C) + + elif var == "C13_NPP_pm": + der_val = (((ds["C13_NPP"]/ds["NPP"].where(ds["NPP"]>0)) / clmPDB) - 1.) * 1e3 + der_val = der_val.where(der_val > min13C) + + elif var == "C14_NPP_pm": + der_val = (((ds["C14_NPP"]/ds["NPP"].where(ds["NPP"]>0)) / clm14C) - 1.) * 1e3 + der_val = der_val.where(der_val > min14C) + + elif var == "C13_TOTVEGC_pm": + der_val = (((ds["C13_TOTVEGC"]/ds["TOTVEGC"].where(ds["TOTVEGC"]>0)) / clmPDB) - 1.) * 1e3 + der_val = der_val.where(der_val > min13C) + + elif var == "C14_TOTVEGC_pm": + der_val = (((ds["C14_TOTVEGC"]/ds["TOTVEGC"].where(ds["TOTVEGC"]>0)) / clm14C) - 1.) * 1e3 + der_val = der_val.where(der_val > min14C) + + elif var == "C13_TOTECOSYSC_pm": + der_val = (((ds["C13_TOTECOSYSC"]/ds["TOTECOSYSC"].where(ds["TOTECOSYSC"]>0)) / clmPDB) - 1.) * 1e3 + der_val = der_val.where(der_val > min13C) + + elif var == "C14_TOTECOSYSC_pm": + der_val = (((ds["C14_TOTECOSYSC"]/ds["TOTECOSYSC"].where(ds["TOTECOSYSC"]>0)) / clm14C) - 1.) * 1e3 + #der_val = der_val.where(der_val > min14C) + else: # Loop through all constituents and sum der_val = 0 @@ -1297,8 +1500,9 @@ def setup_run_mdtf(self): # # Create a dict with all the case info needed for MDTF case_list - # Note that model and convention are hard-coded to CESM because that's all we expect here - # This could be changed by inputing them into ADF with other MDTF-specific variables + # Note that model and convention are hard-coded to CESM + # because that's all we expect here. This could be changed + # by inputing them into ADF with other MDTF-specific variables # case_list_keys = ["CASENAME", "FIRSTYR", "LASTYR", "model", "convention"] @@ -1353,8 +1557,8 @@ def setup_run_mdtf(self): # # Submit the MDTF script in background mode, send output to mdtf.out file - # - mdtf_log = "mdtf.out" # maybe set this to cam_diag_plot_loc: /glade/scratch/${user}/ADF/plots + # maybe set this to cam_diag_plot_loc: /glade/scratch/${user}/ADF/plots + mdtf_log = "mdtf.out" mdtf_exe = mdtf_codebase + os.sep + "mdtf -f " + mdtf_input_settings_filename if copy_files_only: print("\t ...Copy files only. NOT Running MDTF") @@ -1542,4 +1746,24 @@ def my_formatwarning(msg, *args, **kwargs): return xr.open_dataset(fils[0]) #End if # End def -######## \ No newline at end of file + +def save_to_nc(tosave, outname, attrs=None, proc=None): + """Saves xarray variable to new netCDF file""" + + xo = tosave # used to have more stuff here. + # deal with getting non-nan fill values. + if isinstance(xo, xr.Dataset): + enc_dv = {xname: {'_FillValue': None} for xname in xo.data_vars} + else: + enc_dv = {} + #End if + enc_c = {xname: {'_FillValue': None} for xname in xo.coords} + enc = {**enc_c, **enc_dv} + if attrs is not None: + xo.attrs = attrs + if proc is not None: + xo.attrs['Processing_info'] = f"Start from file {origname}. " + proc + xo.to_netcdf(outname, format='NETCDF4', encoding=enc) + +##### +######## diff --git a/lib/adf_info.py b/lib/adf_info.py index 41ebde606..bb55132e6 100644 --- a/lib/adf_info.py +++ b/lib/adf_info.py @@ -45,6 +45,7 @@ #ADF modules: from adf_config import AdfConfig from adf_base import AdfError +import utils #+++++++++++++++++++ #Define Obs class @@ -77,6 +78,8 @@ def __init__(self, config_file, debug=False): #Add CAM climatology info to object: self.__cam_climo_info = self.read_config_var('diag_cam_climo', required=True) + + #Expand CAM climo info variable strings: self.expand_references(self.__cam_climo_info) @@ -133,6 +136,9 @@ def __init__(self, config_file, debug=False): #Initialize ADF variable list: self.__diag_var_list = self.read_config_var('diag_var_list', required=True) + #Initialize ADF variable list: + self.__region_list = self.read_config_var('region_list', required=True) + #Case names: case_names = self.get_cam_info('cam_case_name', required=True) @@ -197,10 +203,36 @@ def __init__(self, config_file, debug=False): # Read hist_str (component.hist_num, eg cam.h0) from the yaml file baseline_hist_str = self.get_baseline_info("hist_str") + if "cam" in baseline_hist_str: + base_comp = "atm" + if "clm" in baseline_hist_str: + base_comp = "lnd" + + #self.__base_comp = base_comp #Check if any time series files are pre-made baseline_ts_done = self.get_baseline_info("cam_ts_done") + baseline_mesh_file = self.get_baseline_info("mesh_file") + self.__baseline_mesh_file = baseline_mesh_file + + #Check if any a FV file exists if using native grid + baseline_latlon_file = self.get_baseline_info("latlon_file") + self.__baseline_latlon_file = baseline_latlon_file + + #Check if any a weights file exists if using native grid, OPTIONAL + baseline_wgts_file = self.get_baseline_info("weights_file") + self.__baseline_wgts_file = baseline_wgts_file + + baseline_regrid_method = self.get_baseline_info("regrid_method") + if baseline_regrid_method == 'conservative': + print("user defined 'conservative', but xesmf has a typo, changing to 'coservative'") + baseline_regrid_method = 'coservative' + self.__baseline_regrid_method = baseline_regrid_method + + baseline_native_grid = self.get_baseline_info("native_grid") + self.__baseline_native_grid = baseline_native_grid + #Check if time series files already exist, #if so don't rely on climo years from history location if baseline_ts_done: @@ -238,7 +270,7 @@ def __init__(self, config_file, debug=False): msg += f"{data_name}, using first found year: {found_eyear_baseline}" print(msg) eyear_baseline = found_eyear_baseline - # End if + # End if baseline time series done # Check if history file path exists: if any(baseline_hist_locs): @@ -267,7 +299,6 @@ def __init__(self, config_file, debug=False): emsg += "\tTry checking the path 'cam_hist_loc' in 'diag_cam_baseline_climo' " emsg += "section in your config file is correct..." self.end_diag_fail(emsg) - file_list = sorted(starting_location.glob("*" + base_hist_str + ".*.nc")) #Check if there are any history files if len(file_list) == 0: @@ -280,6 +311,7 @@ def __init__(self, config_file, debug=False): emsg += " in 'diag_cam_baseline_climo' " emsg += "section in your config file are correct..." self.end_diag_fail(emsg) + base_ds = xr.open_dataset(file_list[0], decode_times=True) # Partition string to find exactly where h-number is # This cuts the string before and after the `{hist_str}.` sub-string @@ -326,6 +358,16 @@ def __init__(self, config_file, debug=False): base_nickname = self.get_baseline_info('case_nickname') if base_nickname is None: base_nickname = data_name + if 'ncols' in base_ds.dims: + print('\t Looks like this is an atmosphere unstructured grid, yeah') + unstruct = True + elif 'lndgrid' in base_ds.dims: + print('\t Looks like this is a land unstructured grid, yeah') + unstruct = True + else: + print('\t Looks like this is a structured lat/lon grid?') + unstruct = False + self.__unstruct_base = unstruct #End if #Grab baseline nickname @@ -357,6 +399,16 @@ def __init__(self, config_file, debug=False): #Plot directory: plot_dir = self.get_basic_info('cam_diag_plot_loc', required=True) + #Unstructured plotting: + unstructured_plotting = self.get_basic_info('unstructured_plotting') + if not unstructured_plotting: + unstructured_plotting = False + self.__unstructured_plotting = unstructured_plotting + + #Mesh file + mesh_file = self.get_basic_info('mesh_file') + self.__mesh_file = mesh_file + #Case names: case_names = self.get_cam_info('cam_case_name', required=True) @@ -392,12 +444,51 @@ def __init__(self, config_file, debug=False): #Check if using pre-made ts files cam_ts_done = self.get_cam_info("cam_ts_done") + #Check if any a FV file exists if using native grid + cam_mesh_files = self.get_cam_info("mesh_file") + if cam_mesh_files is None: + cam_mesh_files = [None]*len(case_names) + self.__cam_mesh_files = cam_mesh_files + + #Check if any a FV file exists if using native grid + cam_latlon_files = self.get_cam_info("latlon_file") + if cam_latlon_files is None: + cam_latlon_files = [None]*len(case_names) + self.__cam_latlon_files = cam_latlon_files + + #Check if any a weights file exists if using native grid, OPTIONAL + cam_wgts_files = self.get_cam_info("weights_file") + if cam_wgts_files is None: + cam_wgts_files = [None]*len(case_names) + self.__cam_wgts_files = cam_wgts_files + + cam_regrid_method = self.get_cam_info("regrid_method") + if cam_regrid_method: + cam_regrid_methods = [] + for regr_method in cam_regrid_method: + if regr_method == 'conservative': + print("user defined 'conservative', but xesmf has a typo, changing to 'coservative'") + cam_regrid_methods.append('coservative') + if regr_method is None: + cam_regrid_methods.append('coservative') + else: + cam_regrid_methods.append(regr_method) + if cam_regrid_method is None: + cam_regrid_method = [None]*len(case_names) + self.__cam_regrid_method = cam_regrid_method + + cam_native_grid = self.get_cam_info("native_grid") + if cam_native_grid is None: + cam_native_grid = [None]*len(case_names) + self.__test_native_grid = cam_native_grid + #Grab case time series file location(s) input_ts_locs = self.get_cam_info("cam_ts_loc", required=True) #Loop over cases: syears_fixed = [] eyears_fixed = [] + unstructs = [] for case_idx, case_name in enumerate(case_names): syear = syears[case_idx] @@ -443,6 +534,13 @@ def __init__(self, config_file, debug=False): #Check if history file path exists: hist_str_case = hist_str[case_idx] + case_comps = [] + if "cam" in hist_str_case: + case_comp = "atm" + case_comps.append("atm") + if "clm" in hist_str_case: + case_comp = "lnd" + case_comps.append("lnd") if any(cam_hist_locs): #Grab first possible hist string, just looking for years of run hist_str = hist_str_case[0] @@ -451,8 +549,6 @@ def __init__(self, config_file, debug=False): starting_location = Path(cam_hist_locs[case_idx]) print(f"\tChecking history files in '{starting_location}'") - file_list = sorted(starting_location.glob('*'+hist_str+'.*.nc')) - #Check if the history file location exists if not starting_location.is_dir(): msg = "Checking history file location:\n" @@ -475,6 +571,18 @@ def __init__(self, config_file, debug=False): emsg += "in 'diag_cam_climo' " emsg += "section in your config file are correct..." self.end_diag_fail(emsg) + + case_ds = xr.open_dataset(file_list[0], decode_times=True) + if 'ncols' in case_ds.dims: + print('\t Looks like this is an atmosphere unstructured grid, yeah') + unstruct = True + if 'lndgrid' in case_ds.dims: + print('\t Looks like this is a land unstructured grid, yeah') + unstruct = True + else: + print('\t Looks like this is a structured lat/lon grid, eh?') + unstruct = False + unstructs.append(unstruct) #Partition string to find exactly where h-number is #This cuts the string before and after the `{hist_str}.` sub-string @@ -549,6 +657,18 @@ def __init__(self, config_file, debug=False): self.__syears = syears_fixed self.__eyears = eyears_fixed + self.__unstruct_test = unstructs + #self.__case_comps = case_comps + + if all(item == base_comp for item in case_comps): + print("All values in the list are the same as the string variable") + self.__model_component = base_comp + else: + msg = "\t ERROR: Looks like the model components are not the same:" + msg += f"\t - Test case(s): {case_comps}; Baseline case: {base_comp}" + raise AdfError(msg) + + #Finally add baseline case (if applicable) for use by the website table #generator. These files will be stored in the same location as the first #listed case. @@ -650,6 +770,13 @@ def num_cases(self): """Return the "num_cases" integer value to the user if requested.""" return self.__num_cases + # Create property needed to return the case nicknames to user: + @property + def model_component(self): + """Return the assumed model component to the user if requested, ie atm or lnd""" + return self.__model_component + + # Create property needed to return "diag_var_list" list to user: @property def diag_var_list(self): @@ -657,6 +784,14 @@ def diag_var_list(self): #Note that a copy is needed in order to avoid having a script mistakenly #modify this variable, as it is mutable and thus passed by reference: return copy.copy(self.__diag_var_list) + + # Create property needed to return "region_list" list to user: + @property + def region_list(self): + """Return a copy of the "region_list" list to the user if requested.""" + #Note that a copy is needed in order to avoid having a script mistakenly + #modify this variable, as it is mutable and thus passed by reference: + return copy.copy(self.__region_list) # Create property needed to return "basic_info" expanded dictionary to user: @property @@ -682,6 +817,34 @@ def baseline_climo_dict(self): #modify this variable, as it is mutable and thus passed by reference: return copy.copy(self.__cam_bl_climo_info) + # Create property needed to return "num_procs" to user: + @property + def unstructured_plotting(self): + """Return the "unstructured_plotting" logical to the user if requested.""" + return self.__unstructured_plotting + + # Create property needed to return "num_procs" to user: + @property + def mesh_file(self): + """Return the "unstructured_plotting" logical to the user if requested.""" + return self.__mesh_file + + + # Create property needed to return the case nicknames to user: + @property + def mesh_files(self): + """Return the test case and baseline nicknames to the user if requested.""" + + #Note that copies are needed in order to avoid having a script mistakenly + #modify these variables, as they are mutable and thus passed by reference: + cam_mesh_files = copy.copy(self.__cam_mesh_files) + + baseline_mesh_file = self.__baseline_mesh_file + + return {"test_mesh_file":cam_mesh_files,"baseline_mesh_file":baseline_mesh_file} + + + # Create property needed to return "num_procs" to user: @property def num_procs(self): @@ -696,6 +859,73 @@ def plot_location(self): #modify this variable: return copy.copy(self.__plot_location) + + # Create property needed to return the case nicknames to user: + @property + def latlon_files(self): + """Return the test case and baseline nicknames to the user if requested.""" + + #Note that copies are needed in order to avoid having a script mistakenly + #modify these variables, as they are mutable and thus passed by reference: + cam_latlon_files = copy.copy(self.__cam_latlon_files) + + baseline_latlon_file = self.__baseline_latlon_file + + return {"test_latlon_file":cam_latlon_files,"baseline_latlon_file":baseline_latlon_file} + + # Create property needed to return the case nicknames to user: + @property + def latlon_wgt_files(self): + """Return the test case and baseline nicknames to the user if requested.""" + + #Note that copies are needed in order to avoid having a script mistakenly + #modify these variables, as they are mutable and thus passed by reference: + cam_wgts_files = copy.copy(self.__cam_wgts_files) + + baseline_wgts_file = self.__baseline_wgts_file + + return {"test_wgts_file":cam_wgts_files,"baseline_wgts_file":baseline_wgts_file} + + # Create property needed to return the case nicknames to user: + @property + def latlon_regrid_method(self): + """Return the test case and baseline nicknames to the user if requested.""" + + #Note that copies are needed in order to avoid having a script mistakenly + #modify these variables, as they are mutable and thus passed by reference: + cam_regrid_method = copy.copy(self.__cam_regrid_method) + + baseline_regrid_method = self.__baseline_regrid_method + + return {"test_regrid_method":cam_regrid_method,"baseline_regrid_method":baseline_regrid_method} + + + + # Create property needed to return the case nicknames to user: + @property + def unstructs(self): + """Return the test case and baseline nicknames to the user if requested.""" + + #Note that copies are needed in order to avoid having a script mistakenly + #modify these variables, as they are mutable and thus passed by reference: + unstruct_tests = copy.copy(self.__unstruct_test) + unstruct_base = self.__unstruct_base + + return {"unstruct_tests":unstruct_tests,"unstruct_base":unstruct_base} + + + # Create property needed to return the case nicknames to user: + @property + def native_grid(self): + """Return the test case and baseline nicknames to the user if requested.""" + + #Note that copies are needed in order to avoid having a script mistakenly + #modify these variables, as they are mutable and thus passed by reference: + test_native_grid = self.__test_native_grid + base_native_grid = self.__baseline_native_grid + + return {"test_native_grid":test_native_grid,"baseline_native_grid":base_native_grid} + # Create property needed to return the climo start (syear) and end (eyear) years to user: @property def climo_yrs(self): @@ -901,4 +1131,4 @@ def get_climo_yrs_from_ts(self, input_ts_loc, case_name): #++++++++++++++++++++ #End Class definition -#++++++++++++++++++++ \ No newline at end of file +#++++++++++++++++++++ diff --git a/lib/adf_obs.py b/lib/adf_obs.py index 2f7be03b2..ddf561cdd 100644 --- a/lib/adf_obs.py +++ b/lib/adf_obs.py @@ -114,7 +114,7 @@ def __init__(self, config_file, debug=False): #Extract the "obs_data_loc" default observational data location: obs_data_loc = self.get_basic_info("obs_data_loc") - + print(obs_data_loc) #Loop over variable list: for var in self.diag_var_list: diff --git a/lib/adf_web.py b/lib/adf_web.py index e39981f8f..fc971de8a 100644 --- a/lib/adf_web.py +++ b/lib/adf_web.py @@ -427,7 +427,7 @@ def jinja_enumerate(arg): #End if #Set main title for website: - main_title = "CAM Diagnostics" + main_title = "CLM Diagnostics" #List of seasons seasons = ["ANN","DJF","MAM","JJA","SON"] @@ -718,7 +718,7 @@ def jinja_enumerate(arg): avail_external_packages = {'MDTF':'mdtf_html_path', 'CVDP':'cvdp_html_path'} #Construct index.html - index_title = "AMP Diagnostics Prototype" + index_title = "CLM Diagnostics" index_tmpl = jinenv.get_template('template_index.html') index_rndr = index_tmpl.render(title=index_title, case_name=web_data.case, @@ -768,7 +768,7 @@ def jinja_enumerate(arg): #End for (model case loop) #Create multi-case site: - main_title = "ADF Diagnostics" + main_title = "CLM Diagnostics" main_tmpl = jinenv.get_template('template_multi_case_index.html') main_rndr = main_tmpl.render(title=main_title, case_sites=case_sites, diff --git a/lib/ldf_variable_defaults.yaml b/lib/ldf_variable_defaults.yaml new file mode 100644 index 000000000..d72665af2 --- /dev/null +++ b/lib/ldf_variable_defaults.yaml @@ -0,0 +1,575 @@ + +#This file lists out variable-specific defaults +#for plotting and observations. These defaults +#are: +# +# PLOTTING: +# +# colormap -> The colormap that will be used for filled contour plots. +# contour_levels -> A list of the specific contour values that will be used for contour plots. +# Cannot be used with "contour_levels_range". +# contour_levels_range -> The contour range that will be used for plots. +# Values are min, max, and stride. Cannot be used with "contour_levels". +# diff_colormap -> The colormap that will be used for filled contour different plots +# diff_contour_levels -> A list of the specific contour values thta will be used for difference plots. +# Cannot be used with "diff_contour_range". +# diff_contour_range -> The contour range that will be used for difference plots. +# Values are min, max, and stride. Cannot be used with "diff_contour_levels". +# scale_factor -> Amount to scale the variable (relative to its "raw" model values). +# add_offset -> Amount of offset to add to the variable (relatie to its "raw" model values). +# new_unit -> Variable units (if not using the "raw" model units). +# mpl -> Dictionary that contains keyword arguments explicitly for matplotlib +# +# mask -> Setting that specifies whether the variable should be masked. +# Currently only accepts "landmask", which means the variable will be masked +# everywhere that isn't land. +# +# +# OBSERVATIONS: +# +# obs_file -> Path to observations file. If only the file name is given, then the file is assumed to +# exist in the path specified by "obs_data_loc" in the config file. +# obs_name -> Name of the observational dataset (mostly used for plotting and generated file naming). +# If this isn't present then the obs_file name is used. +# obs_var_name -> Variable in the observations file to compare against. If this isn't present then the +# variable name is assumed to be the same as the model variable name. +# +# +# +# WEBSITE: +# +# category -> The website category the variable will be placed under. +# +# +# DERIVING: +# +# derivable_from -> If not present in the available output files, the variable can be derived from +# other variables that are present (e.g. PRECT can be derived from PRECC and PRECL), +# which are specified in this list +# NOTE: this is not very flexible at the moment! It can only handle variables that +# are sums of the constituents. Futher flexibility is being explored. +# +# +# Final Note: Please do not modify this file unless you plan to push your changes back to the ADF repo. +# If you would like to modify this file for your personal ADF runs then it is recommended +# to make a copy of this file, make modifications in that copy, and then point the ADF to +# it using the "defaults_file" config variable. +# +#+++++++++++ + +#+++++++++++++ +# Available Land Default Plot Types +#+++++++++++++ + +default_ptypes: ["Tables","LatLon","TimeSeries", + "Arctic","RegionalClimo","RegionalTimeSeries","Special"] + +#+++++++++++++ +# Constants +#+++++++++++++ + +#seconds per day : +spd: 86400 +diff_levs: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] +pct_diff_contour_levels: [-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20] + +#+++++++++++++ +# Category: Atmosphere +#+++++++++++++ + +TSA: # 2m air temperature + category: "Atmosphere" + colormap: "OrRd" + contour_levels_range: [250, 310, 10] + diff_colormap: "coolwarm" + pct_diff_colormap: "coolwarm" + pct_diff_contour_levels: [-3,-2,-1,-0.5,-0.2,-0.1,0,0.1,0.2,0.5,1,2,3] + scale_factor: 1 + add_offset: 0 + new_unit: "K" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/WILLMOTT_ALLMONS_climo.nc" + obs_name: "WILMONT" + obs_var_name: "TSA" # K + +PREC: # RAIN + SNOW + category: "Atmosphere" + colormap: "cubehelix_r" + derivable_from: ["RAIN","SNOW"] + scale_factor: 86400 + add_offset: 0 + new_unit: "mm d$^{-1}$" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/GPCPv2.3_ALLMONS_climo.nc" + obs_name: "GCPCv2.3" + obs_var_name: "PREC" #mm/d + mpl: + colorbar: + label : "mm d$^{-1}$" + diff_colormap: "BrBG" + pct_diff_colormap: "PuOr_r" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + +FLDS: # atmospheric longwave radiation + category: "Atmosphere" + colormap: "Oranges" + contour_levels_range: [100, 500, 25] + diff_colormap: "BrBG" + diff_contour_range: [-20, 20, 2] + scale_factor: 1 + add_offset: 0 + new_unit: "W m$^{-2}$" + mpl: + colorbar: + label : "W m$^{-2}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/CERESed4.2-rlds_ALLMONS_climo.nc" + obs_name: "CERESed4.2" + obs_var_name: "rlds" #W/m2 + +FSDS: # atmospheric incident solar radiation + category: "Atmosphere" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + new_unit: "W m$^{-2}$" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/CERESed4.2-rsds_ALLMONS_climo.nc" + obs_name: "CERESed4.2" + obs_var_name: "rsds" #W/m2 + +WIND: # atmospheric air temperature + category: "Atmosphere" + +QBOT: # atmospheric specific humidity + category: "Atmosphere" + scale_factor: 1 + add_offset: 0 + #new_unit: "W m$^{-2}$" + +TBOT: + category: "Atmosphere" + colormap: "OrRd" + contour_levels_range: [250, 310, 10] + +TREFMNAV: # daily minimum of average 2m temperature + category: "Atmosphere" + colormap: "OrRd" + contour_levels_range: [250, 310, 10] + + +TREFMXAV: # daily maximum of average 2m temperature + category: "Atmosphere" + colormap: "OrRd" + contour_levels_range: [250, 310, 10] + + +#+++++++++++ +# Category: Surface fluxes +#+++++++++++ + +ASA: # all-sky albedo:FSR/FSDS + category: "Surface fluxes" + colormap: "RdBu_r" + diff_colormap: "BrBG" + derivable_from: ["FSR", "FSDS"] + new_unit: "% reflected" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + # TODO, Keith will clarify what obs to use and we'll also have to figure out grid weights for regional averaging + ## First file has weights, but no clear variable for ASA + # obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/T42_MODIS_ALLMONS_climo.070523.nc" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/modisradweighted.nc" + obs_data: "MODIS" + obs_var_name: "BRDALB" + +FSA: # absorbed solar radiation + category: "Surface fluxes" + new_unit: "W m$^{-2}$" + +FSH: # sensible heat + category: "Surface fluxes" + new_unit: "W m$^{-2}$" + +RNET: # Net Radiation: FSA-FIRA + category: "Surface fluxes" + derivable_from: ["FSA", "FIRA"] + new_unit: "W m$^{-2}$" + +ET: # latent heat: FCTR+FCEV+FGEV + category: "Surface fluxes" + derivable_from: ["FCTR","FCEV","FGEV"] + colormap: "Purples" + contour_levels_range: [0, 220, 10] + diff_colormap: "BrBG" + diff_contour_range: [-45, 45, 5] + scale_factor: 1 + add_offset: 0 + new_unit: "W m$^{-2}$" + mpl: + colorbar: + label : "W m$^{-2}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "BrBG" + obs_file: "/glade/campaign/cgd/tss/people/oleson/lnd_diag_data/obs_data/MR_LHF_0.9x1.25_ALLMONS_climo.nc" + obs_name: "FLUXNET" + obs_var_name: "LHF" #W m$^{-2}$ + +FCTR: # Canopy transpiration latent heat flux + category: "Surface fluxes" + scale_factor: 1 + add_offset: 0 + new_unit: "W m$^{-2}$" + mpl: + colorbar: + label : "W m$^{-2}$" + +FCEV: # Canopy evaporation of latent heat flux + category: "Surface fluxes" + scale_factor: 1 + add_offset: 0 + new_unit: "W m$^{-2}$" + mpl: + colorbar: + label : "W m$^{-2}$" + +FGEV: # Ground evaporation latent heat flux + category: "Surface fluxes" + scale_factor: 1 + add_offset: 0 + new_unit: "W m$^{-2}$" + mpl: + colorbar: + label : "W m$^{-2}$" + +DSTFLXT: # total surface dust emission + category: "Surface fluxes" + colormap: "copper_r" + diff_colormap: "BrBG_r" + scale_factor: 86400 + add_offset: 0 + new_unit: "kg m$^{-2}$ d$^{-1}$" + mpl: + colorbar: + label : "kg m$^{-2}$ d$^{-1}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor_table: 0.000365 #days to years, kg/m2 to Pg globally + avg_method: 'sum' + table_unit: "Pg y$^{-1}$" + +MEG_isoprene: + category: "Surface fluxes" + colormap: "Blues" + diff_colormap: "BrBG_r" + scale_factor: 86400 + add_offset: 0 + new_unit: "kg m$^{-2}$ d$^{-1}$" + mpl: + colorbar: + label : "kg m$^{-2}$ d$^{-1}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PuOr_r" + scale_factor_table: 0.365 #days to years, kg/m2 to Tg globally + avg_method: 'sum' + table_unit: "Tg y$^{-1}$" + + +#+++++++++++ +# Category: Hydrology +#+++++++++++ +FSNO: # fraction of ground covered by snow + category: "Hydrology" + diff_contour_range: [-50,50,10] + +H2OSNO: # SNOWICE + SNOWLIQ + category: "Hydrology" + diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + +SNOWDP: # snow height + category: "Hydrology" + contour_levels: [-15.,-10.,-5.,-1.,-0.5,-0.1,0.,0.1,0.5,1.,5.,10.,15.] + diff_contour_levels: [-10.,-5.,-1.,-0.5,-0.1,0.,0.1,0.5,1.,5.,10.] + new_unit: "m" + +TOTRUNOFF: # total liquid runoff + category: "Hydrology" + derivable_from: ["QOVER","QDRAI","QRGWL"] #TODO, check accuracy + colormap: "Blues" + scale_factor: 86400 + add_offset: 0 + new_unit: "mm d$^{-1}$" + contour_levels: [-0.1,0.,0.1,0.2,0.3,1.,2.,3.,] + diff_contour_range: [-1.,1.,0.1] + mpl: + colorbar: + label : "mm d$^{-1}$" + +SOILWATER_10CM: # soil liquid water + ice in top 10cm of soil + category: "Hydrology" + colormap: "Blues" + +TWS: # Terrestrial water storage + category: "Hydrology" + colormap: "Blues" + +QRUNOFF_TO_COUPLER: # runoff to coupler + category: "Hydrology" + colormap: "Blues" + scale_factor: 86400 + add_offset: 0 + new_unit: "mm d$^{-1}$" + contour_levels: [-0.1,0.,0.1,0.2,0.3,1.,2.,3.,] + diff_contour_range: [-1.,1.,0.1] + mpl: + colorbar: + label : "mm d$^{-1}$" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/GRUN_ALLMONS_climo.nc" + obs_name: "GRUN" + obs_var_name: "RUNOFF" #mm/d + +#+++++++++++ +# Category: Vegetation +#+++++++++++ +BTRANMN: # Transpiration beta factor + category: "Vegetation" # Or hydrology? + +ELAI: # exposed one-sided leaf area index + category: "Vegetation" + colormap: "gist_earth_r" + contour_levels_range: [0., 8., 1.0] + diff_colormap: "PiYG" + diff_contour_range: [-2.,2.,0.25] + new_unit: "m${-2}$ m${-2}$" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/MODIS_LAI_ALLMONS_climo.nc" + obs_name: "MODIS" + obs_var_name: "TLAI" + + +HTOP: # canopy top height + category: "Vegetation" + + +TSAI: # total one-sided stem area index + category: "Vegetation" + new_unit: "m${-2}$ m${-2}$" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/MODIS_LAI_ALLMONS_climo.nc" + obs_name: "MODIS" + obs_var_name: "TSAI" + +#+++++++++++ +# Category: Carbon +#+++++++++++ +GPP: # Gross Primary Production + category: "Carbon" + colormap: "gist_earth_r" + contour_levels_range: [0., 12., 0.5] + diff_colormap: "PiYG" + diff_contour_range: [-4.,4.,0.5] + scale_factor: 86400 #seconds to days + add_offset: 0 + new_unit: "gC m$^{-2}$ d$^{-1}$" + mpl: + colorbar: + label : "gC m$^{-2}$ d$^{-1}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PiYG" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC y$^{-1}$" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/MR_GPP_0.9x1.25_ALLMONS_climo.nc" + obs_name: "FLUXNET" + obs_var_name: "GPP" #gC m$^{-2}$ d$^{-1}$ + +FPSN: # Photosynthesis umol CO2 m-2 s-1 -> gC/m2/2 +#TODO, make sure this works as expected + category: "Carbon" + colormap: "gist_earth_r" + contour_levels_range: [0., 12., 0.5] + diff_colormap: "PiYG" + diff_contour_range: [-4.,4.,0.5] + scale_factor: 7200000000 #86400/1.2e-5 #seconds to days, umol CO2 to gC + add_offset: 0 + new_unit: "gC m$^{-2}$ d$^{-1}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PiYG" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC y$^{-1}$" + obs_file: "/glade/campaign/cgd/tss/people/oleson/FROM_LMWG/diag/lnd_diag4.2/obs_data/MR_GPP_0.9x1.25_ALLMONS_climo.nc" + obs_name: "FLUXNET" + obs_var_name: "GPP" #gC m$^{-2}$ d$^{-1}$ + + +AR: # Autotrophic Respiration + category: "Carbon" + colormap: "gist_earth_r" + contour_levels_range: [0., 3., 0.25] + diff_colormap: "PiYG" + diff_contour_range: [-1.5, 1.5, 0.25] + scale_factor: 86400 + add_offset: 0 + new_unit: "gC m$^{-2}$ d$^{-1}$" + mpl: + colorbar: + label : "gC m$^{-2}$ d$^{-1}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PiYG" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC y$^{-1}$" + +NPP: # Net Primary Production + category: "Carbon" + colormap: "gist_earth_r" + contour_levels_range: [0., 3., 0.25] + diff_colormap: "PiYG" + diff_contour_range: [-1.5, 1.5, 0.25] + scale_factor: 86400 + add_offset: 0 + new_unit: "gC m$^{-2}$ d$^{-1}$" + mpl: + colorbar: + label : "gC m$^{-2}$ d$^{-1}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PiYG" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC y$^{-1}$" + +NEE: # Net Ecosystem Eschange + category: "Carbon" + diff_colormap: "PiYG" + scale_factor: 86400 + add_offset: 0 + new_unit: "gC m$^{-2}$ d$^{-1}$" + mpl: + colorbar: + label : "gC m$^{-2}$ d$^{-1}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PiYG" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC y$^{-1}$" + +NBP: # Net Biome Production + category: "Carbon" + colormap: "gist_earth_r" + diff_colormap: "PiYG" + scale_factor: 86400 + add_offset: 0 + new_unit: "gC m$^{-2}$ d$^{-1}$" + mpl: + colorbar: + label : "gC m$^{-2}$ d$^{-1}$" + pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PiYG" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'cumsum' # in TS NBP is cum sum, table just uses sum implicitly (not mean) + table_unit: "PgC y$^{-1}$" + ts_unit: "PgC" #global TS uses cumulative NBP in PgC + +TOTECOSYSC_1m: + category: "Carbon" + scale_factor_table: 0.000000001 #g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC" + +TOTSOMC_1m: + category: "Carbon" + scale_factor_table: 0.000000001 #g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC" + +TOTVEGC: + category: "Carbon" + scale_factor_table: 0.000000001 #g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC" + +# C Isotopes +C13_GPP_pm: + category: "Carbon" + derivable_from: ["C13_GPP","GPP"] + new_unit: "per mile PDB" + +C14_GPP_pm: #TODO, check that calculations are correct + category: "Carbon" + derivable_from: ["C14_GPP","GPP"] + new_unit: "per mile" + +C13_TOTVEGC_pm: #TODO, check that calculations are correct + category: "Carbon" + derivable_from: ["C13_TOTVEGC","TOTVEGC"] + new_unit: "per mile PDB" + +C14_TOTVEGC_pm: #TODO, check that calculations are correct + category: "Carbon" + derivable_from: ["C14_TOTVEGC","TOTVEGC"] + new_unit: "per mile" + +C13_TOTSOMC_pm: #TODO, check that calculations are correct + category: "Carbon" + derivable_from: ["C13_TOTSOMC","TOTSOMC"] + new_unit: "per mile PDB" + +C14_TOTSOMC_pm: #TODO, check that calculations are correct + category: "Carbon" + derivable_from: ["C14_TOTSOMC","TOTSOMC"] + new_unit: "per mile" + +C13_TOTECOSYSC_pm: #TODO, check that calculations are correct + category: "Carbon" + derivable_from: ["C13_TOTECOSYSC","TOTECOSYSC"] + new_unit: "per mile PDB" + +C14_TOTECOSYSC_pm: #TODO, check that calculations are correct + category: "Carbon" + derivable_from: ["C14_TOTECOSYSC","TOTECOSYSC"] + new_unit: "per mile" + +C13_TOTLITC_pm: #TODO, check that calculations are correct + category: "Carbon" + derivable_from: ["C13_TOTLITC","TOTLITC"] + new_unit: "per mile PDB" + +C14_TOTLITC_pm: #TODO, check that calculations are correct + category: "Carbon" + derivable_from: ["C14_TOTLITC","TOTLITC"] + new_unit: "per mile" + + +#+++++++++++ +# Category: CROP +#+++++++++++ +GRAINC_TO_FOOD: + category: "Crop" + #contour_levels_range: [0., 3., 0.25] + diff_colormap: "PiYG" + #diff_contour_range: [-1.5, 1.5, 0.25] + scale_factor: 86400 + add_offset: 0 + new_unit: "gC m$^{-2}$ d$^{-1}$" + mpl: + colorbar: + label : "gC m$^{-2}$ d$^{-1}$" + #pct_diff_contour_levels: [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] + pct_diff_colormap: "PiYG" + scale_factor_table: 0.000000365 #days to years, g/m2 to Pg globally + avg_method: 'sum' + table_unit: "PgC y$^{-1}$" + + +#+++++++++++ +# Category: Soils +#+++++++++++ +ALTMAX: # Active Layer Thickness + category: "Soils" + + +SOILWATER_10CM: # soil liquid water + ice in top 10cm of soil + category: "Soils" # or hydrology? + +TSOI_10CM: # Soil temperature, 0-10 cm + category: "Soils" + + + +#End of File diff --git a/lib/plot_uxarray_h1.ipynb b/lib/plot_uxarray_h1.ipynb new file mode 100644 index 000000000..4368ce1be --- /dev/null +++ b/lib/plot_uxarray_h1.ipynb @@ -0,0 +1,1447 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "39545902-0870-4a3f-93f1-493e56403d38", + "metadata": {}, + "source": [ + "### test for plotting pft level data on h1 files\n", + "Created by Will Wieder\n", + "Improved by Orhan Eroglu\n", + "March 2025" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b75e38a9-54ff-438b-91cd-2f72ef3abd95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) 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function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.5.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in 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handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var 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events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025.3.0\n" + ] + } + ], + "source": [ + "import os, sys\n", + "import shutil\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import numpy as np\n", + "import xarray as xr\n", + "import xesmf as xe\n", + "\n", + "# Helpful for plotting only\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import uxarray as ux #need npl 2024a or later\n", + "import geoviews.feature as gf\n", + "\n", + "print(ux.__version__)\n", + "#sys.path.append('/glade/u/home/wwieder/python/adf/lib/plotting_functions.py')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "07650a02-db90-4ee9-8880-e3f4ac140871", + "metadata": {}, + "outputs": [], + "source": [ + "# Load datataset\n", + "# TODO, load with adf tools and config file options\n", + "gppfile='/glade/derecho/scratch/wwieder/ADF/b.e30_beta04.BLT1850.ne30_t232_wgx3.121/climo/b.e30_beta04.BLT1850.ne30_t232_wgx3.121_GPP_climo.nc'\n", + "h0_file='/glade/derecho/scratch/wwieder/archive/ctsm5.4_5.3.068_PPEcal115_116_HIST/lnd/hist/ctsm5.4_5.3.068_PPEcal115_116_HIST.clm2.h0a.1930-11.nc'\n", + "#laih1file='/glade/derecho/scratch/wwieder/ctsm53n04ctsm52028_ne30pg3t232_hist.clm2.h1.TLAI.1860s.nc'\n", + "laih1file='/glade/derecho/scratch/wwieder/TLAI_cat/ctsm5.4_5.3.068_PPEcal115_116_HIST.clm2.h1a.TLAI.1850s.nc'\n", + "case = 'ctsm5.4_5.3.068_PPEcal115_116_HIST'\n", + "\n", + "mesh0 = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'\n", + "\n", + "#ux file for plotting\n", + "uxds0 = ux.open_dataset(mesh0, h0_file).max('time')\n", + "uxds1 = ux.open_dataset(mesh0, laih1file).max('time')\n", + "\n", + "# Assign coords to uxds0, which will be needed later for align() operation\n", + "n_face_coords = np.arange(1,(uxds1.pfts1d_ixy.max().astype(int)+1))\n", + "uxds0 = uxds0.assign_coords({'n_face': ('n_face', n_face_coords)})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "66066158-c9d6-4dba-9c70-53aeeae4456a", + "metadata": {}, + "outputs": [], + "source": [ + "def reshape_ux_h1(uxds1, uxds0, var='TLAI', npft=15):\n", + " \"\"\"\n", + " Reshape unstructured data from h1 history files:\n", + " - Inputs 1d data from uxarray dataset (pft) into\n", + " - Returns 2d uxarray dataset (pft x n_face)\n", + " - Also include area + landfrac data, taken here from h0 dataset\n", + "\n", + " Requires h1 and h0 datasets that include the target variable\n", + " By default this function only runs on the native pfts\n", + "\n", + " \"\"\"\n", + "\n", + " for i in range(1, npft):\n", + " temp = uxds1.where(uxds1.pfts1d_itype_veg==i, drop=True)\n", + " # TODO, PFT weights should be time evolving, but they aren't here\n", + " # Rename coord, since the pft dimension is not meaningful\n", + " temp= temp.rename({'pft': 'n_face'})\n", + "\n", + " # assign values from pfts1d_ixy to n_face\n", + " temp['n_face'] = temp.pfts1d_ixy.astype(int)\n", + " temp.assign_coords({\"npft\": i})\n", + "\n", + " # combine along PFT variable\n", + " if i == 1:\n", + " uxdsOut = temp\n", + " else:\n", + " uxdsOut = xr.concat([uxdsOut, temp], dim=\"npft\")\n", + "\n", + " uxdsOut.uxgrid = temp.uxgrid\n", + " uxdsOut, _ = xr.align(uxdsOut, uxds0[var], join=\"right\")\n", + " # now copy over area & landfrac\n", + " uxdsOut['area'] = uxds0.area\n", + " uxdsOut['landfrac'] = uxds0.landfrac\n", + " return uxdsOut" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a7921508-b630-4bc2-8549-747ad577184f", + "metadata": {}, + "outputs": [], + "source": [ + "# Call the reshape_ux_h1 function\n", + "npft=15\n", + "var='TLAI'\n", + "uxdsOut = reshape_ux_h1(uxds1, uxds0, var, npft)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f847e56b-d807-4dab-8be1-e3d1cfeb5b71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nOr hard code them\\npft_names = ['NET Temperate', 'NET Boreal', 'NDT Boreal',\\n 'BET Tropical', 'BET Temperate', 'BDT Tropical',\\n 'BDT Temperate', 'BDT Boreal', 'BES Temperate',\\n 'BDS Temperate', 'BDS Boreal', 'C3 Grass Arctic',\\n 'C3 Grass', 'C4 Grass', 'UCrop UIrr']\\n\"" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read in pft names\n", + "pft_constants = xr.open_dataset(\n", + " '/glade/campaign/cesm/cesmdata/cseg/inputdata/lnd/clm2/paramdata/ctsm60_params.c241017.nc')\n", + "pft_names = pft_constants.pftname\n", + "\n", + "'''\n", + "Or hard code them\n", + "pft_names = ['NET Temperate', 'NET Boreal', 'NDT Boreal',\n", + " 'BET Tropical', 'BET Temperate', 'BDT Tropical',\n", + " 'BDT Temperate', 'BDT Boreal', 'BES Temperate',\n", + " 'BDS Temperate', 'BDS Boreal', 'C3 Grass Arctic',\n", + " 'C3 Grass', 'C4 Grass', 'UCrop UIrr']\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "55ceea85-e3e2-4e2e-a88c-03c1313acf31", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO Add this to ADF plotting functions?\n", + "def plot_ux_survival(uxdsOut, case=case, var='TLAI', npft = 15,\n", + " pft_names = pft_names, min_pft_wgt = 0.05):\n", + " '''\n", + " Accepts reshaped h1 file from unstructured grid\n", + " - Also requires case name, variable, and PFT names (strings), and\n", + " - min value for PFT weights (fraction of grid cell) as a mask\n", + " Currently hard coded to make nice PFT survival plots, but \n", + " Could be adapted for more general subgid output\n", + " '''\n", + "\n", + " # Basic plot settings\n", + " transform = ccrs.PlateCarree()\n", + " proj = ccrs.PlateCarree()\n", + " cmap = plt.cm.viridis_r\n", + " cmap.set_under(color='deeppink')\n", + " cmap = cmap.resampled(7)\n", + " levels = [0.1, 1, 2, 3, 4, 5, 6, 7]\n", + "\n", + " # create figure object\n", + " fig, axs = plt.subplots(5,3,\n", + " facecolor=\"w\",\n", + " constrained_layout=True,\n", + " subplot_kw=dict(projection=proj) )\n", + "\n", + " axs=axs.flatten()\n", + "\n", + " # Loop over pfts\n", + " for i in range((npft-1)):\n", + " # Calculate weights bases on area, landfrac and min_pft_wgt\n", + " pft_wgt = uxdsOut.pfts1d_wtgcell.isel(npft=i)\n", + " pft_wgt = pft_wgt.where(pft_wgt >= min_pft_wgt)\n", + " wgts = uxdsOut.area * uxdsOut.landfrac * pft_wgt\n", + " wgts = wgts / wgts.sum()\n", + "\n", + " # Plots where LAI > min_wgt on grid\n", + " plot_var = uxdsOut[var].isel(npft=i).where(pft_wgt >= min_pft_wgt)\n", + " ac = plot_var.to_polycollection(projection=proj)\n", + " ac.set_cmap(cmap)\n", + " ac.set_antialiased(False)\n", + " ac.set_transform(transform)\n", + " ac.set_clim(vmin=0.1,vmax=6.9)\n", + " axs[i].add_collection(ac)\n", + "\n", + " # Add titles (pft names) & statistics (mean LAI & survival)\n", + " mean = str(np.round((uxdsOut[var].isel(npft=i)*wgts).sum().values,2))\n", + " dead = ((uxdsOut[var].isel(npft=i)<0.1)*wgts).sum()\n", + " live = ((uxdsOut[var].isel(npft=i)>0.1)*wgts).sum()\n", + " livefrac = str(np.round((live/(live+dead)).values,2))\n", + " axs[i].set_title(str(pft_names[(1+i)].data)[2:40], loc='left',size=6)\n", + " axs[i].text(-30, -45,'mean = '+ mean, fontsize=5)\n", + " axs[i].text(-45, -60,'live frac = '+livefrac,fontsize=5)\n", + "\n", + " # make panels look nice\n", + " for a in axs:\n", + " a.coastlines().set_linewidth(0.1)\n", + " a.set_global()\n", + " a.spines['geo'].set_linewidth(0.1) #cartopy's recommended method\n", + " a.set_extent([-180, 180, -65, 86])\n", + "\n", + " # add color bar with case name\n", + " fig.set_layout_engine(\"compressed\")\n", + " cbar_ax = fig.add_axes([0.94, 0.06, 0.02, 0.88])\n", + " cbar = fig.colorbar(ac, cax=cbar_ax, pad=0.1, shrink=0.7, aspect=40, extend='both')\n", + " cbar.ax.tick_params(labelsize=9)\n", + " cbar.set_label(label=(\"max LAI \"+case), size=9, weight='bold')\n", + "\n", + " return fig\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1d2c3658-48a9-4ca9-931d-a592c46e1c60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- wrote pft TLAI figure --\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Call plotting function\n", + "fig = plot_ux_survival (uxdsOut = uxdsOut, case = case, var = var,\n", + " pft_names = pft_names, min_pft_wgt = 0.1)\n", + "save = True\n", + "# TODO add ADF plotting and webpage hooks\n", + "if (save == True):\n", + " fig.savefig('h1_test', bbox_inches='tight', dpi=300)\n", + " print('-- wrote pft '+var+' figure --')\n", + "plt.show() ;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93c009b2-f94f-49d5-8810-16c44a3f4c92", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:ldf_v0.0]", + "language": "python", + "name": "conda-env-ldf_v0.0-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lib/plot_uxarray_h1_raster_better.ipynb b/lib/plot_uxarray_h1_raster_better.ipynb new file mode 100644 index 000000000..88eb360eb --- /dev/null +++ b/lib/plot_uxarray_h1_raster_better.ipynb @@ -0,0 +1,1464 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "39545902-0870-4a3f-93f1-493e56403d38", + "metadata": {}, + "source": [ + "### test for plotting pft level data on h1 files\n", + "Created by Will Wieder\n", + "Improved by Orhan Eroglu\n", + "March 2025" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b75e38a9-54ff-438b-91cd-2f72ef3abd95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + " const py_version = '3.7.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " const reloading = false;\n", + " const Bokeh = root.Bokeh;\n", + "\n", + " // Set a timeout for this load but only if we are not already initializing\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " // Don't load bokeh if it is still initializing\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " // There is nothing to load\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error(e) {\n", + " const src_el = e.srcElement\n", + " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", + " }\n", + "\n", + " const skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " const existing_stylesheets = []\n", + " const links = document.getElementsByTagName('link')\n", + " for (let i = 0; i < links.length; i++) {\n", + " const link = links[i]\n", + " if (link.href != null) {\n", + " existing_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", + " on_load()\n", + " continue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " const scripts = document.getElementsByTagName('script')\n", + " for (let i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + " existing_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (let i = 0; i < js_modules.length; i++) {\n", + " const url = js_modules[i];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " const url = js_exports[name];\n", + " const escaped = encodeURI(url)\n", + " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", + " if (!window.requirejs) {\n", + " on_load();\n", + " }\n", + " continue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.holoviz.org/panel/1.6.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.2.min.js\", \"https://cdn.holoviz.org/panel/1.6.2/dist/panel.min.js\"];\n", + " const js_modules = [];\n", + " const js_exports = {};\n", + " const css_urls = [];\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " try {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " } catch(e) {\n", + " if (!reloading) {\n", + " throw e;\n", + " }\n", + " }\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + " var NewBokeh = root.Bokeh;\n", + " if (Bokeh.versions === undefined) {\n", + " Bokeh.versions = new Map();\n", + " }\n", + " if (NewBokeh.version !== Bokeh.version) {\n", + " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + " }\n", + " root.Bokeh = Bokeh;\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.7.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.6.2/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.2.min.js\", \"https://cdn.holoviz.org/panel/1.6.2/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " })\n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var 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Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " 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ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " // If the timeout and bokeh was not successfully loaded we reset\n", + " // everything and try loading again\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " root._bokeh_is_loading = 0\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + " if (root.Bokeh) {\n", + " root.Bokeh = undefined;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = 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function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + 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(Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = 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document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', 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events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025.6.0\n" + ] + } + ], + "source": [ + "import os, sys\n", + "import shutil\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import numpy as np\n", + "import xarray as xr\n", + "import xesmf as xe\n", + "\n", + "# Helpful for plotting only\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import uxarray as ux #need npl 2024a or later\n", + "import geoviews.feature as gf\n", + "\n", + "print(ux.__version__)\n", + "#sys.path.append('/glade/u/home/wwieder/python/adf/lib/plotting_functions.py')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "07650a02-db90-4ee9-8880-e3f4ac140871", + "metadata": {}, + "outputs": [], + "source": [ + "# Load datataset\n", + "# TODO, load with adf tools and config file options\n", + "h0_file='/glade/derecho/scratch/wwieder/archive/ctsm5.4_5.3.068_PPEcal115_116_HIST/lnd/hist/ctsm5.4_5.3.068_PPEcal115_116_HIST.clm2.h0a.1930-11.nc'\n", + "#laih1file='/glade/derecho/scratch/wwieder/ctsm53n04ctsm52028_ne30pg3t232_hist.clm2.h1.TLAI.1860s.nc'\n", + "laih1file='/glade/derecho/scratch/wwieder/TLAI_cat/ctsm5.4_5.3.068_PPEcal115_116_HIST.clm2.h1a.TLAI.1850s.nc'\n", + "case = 'ctsm5.4_5.3.068_PPEcal115_116_HIST'\n", + "\n", + "mesh0 = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'\n", + "\n", + "#ux file for plotting\n", + "uxds0 = ux.open_dataset(mesh0, h0_file).max('time')\n", + "uxds1 = ux.open_dataset(mesh0, laih1file).max('time')\n", + "\n", + "# Assign coords to uxds0, which will be needed later for align() operation\n", + "n_face_coords = np.arange(1,(uxds1.pfts1d_ixy.max().astype(int)+1))\n", + "uxds0 = uxds0.assign_coords({'n_face': ('n_face', n_face_coords)})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "66066158-c9d6-4dba-9c70-53aeeae4456a", + "metadata": {}, + "outputs": [], + "source": [ + "def reshape_ux_h1(uxds1, uxds0, var='TLAI', npft=15):\n", + " \"\"\"\n", + " Reshape unstructured data from h1 history files:\n", + " - Inputs 1d data from uxarray dataset (pft) into\n", + " - Returns 2d uxarray dataset (pft x n_face)\n", + " - Also include area + landfrac data, taken here from h0 dataset\n", + "\n", + " Requires h1 and h0 datasets that include the target variable\n", + " By default this function only runs on the native pfts\n", + "\n", + " \"\"\"\n", + "\n", + " for i in range(1, npft):\n", + " temp = uxds1.where(uxds1.pfts1d_itype_veg==i, drop=True)\n", + " # TODO, PFT weights should be time evolving, but they aren't here\n", + " # Rename coord, since the pft dimension is not meaningful\n", + " temp= temp.rename({'pft': 'n_face'})\n", + "\n", + " # assign values from pfts1d_ixy to n_face\n", + " temp['n_face'] = temp.pfts1d_ixy.astype(int)\n", + " temp.assign_coords({\"npft\": i})\n", + "\n", + " # combine along PFT variable\n", + " if i == 1:\n", + " uxdsOut = temp\n", + " else:\n", + " uxdsOut = xr.concat([uxdsOut, temp], dim=\"npft\")\n", + "\n", + " uxdsOut.uxgrid = temp.uxgrid\n", + " uxdsOut, _ = xr.align(uxdsOut, uxds0[var], join=\"right\")\n", + " # now copy over area & landfrac\n", + " uxdsOut['area'] = uxds0.area\n", + " uxdsOut['landfrac'] = uxds0.landfrac\n", + " return uxdsOut" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a7921508-b630-4bc2-8549-747ad577184f", + "metadata": {}, + "outputs": [], + "source": [ + "# Call the reshape_ux_h1 function\n", + "npft=15\n", + "var='TLAI'\n", + "uxdsOut = reshape_ux_h1(uxds1, uxds0, var, npft)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f847e56b-d807-4dab-8be1-e3d1cfeb5b71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nOr hard code them\\npft_names = ['NET Temperate', 'NET Boreal', 'NDT Boreal',\\n 'BET Tropical', 'BET Temperate', 'BDT Tropical',\\n 'BDT Temperate', 'BDT Boreal', 'BES Temperate',\\n 'BDS Temperate', 'BDS Boreal', 'C3 Grass Arctic',\\n 'C3 Grass', 'C4 Grass', 'UCrop UIrr']\\n\"" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Read in pft names\n", + "pft_constants = xr.open_dataset(\n", + " '/glade/campaign/cesm/cesmdata/cseg/inputdata/lnd/clm2/paramdata/ctsm60_params.c241017.nc')\n", + "pft_names = pft_constants.pftname\n", + "\n", + "'''\n", + "Or hard code them\n", + "pft_names = ['NET Temperate', 'NET Boreal', 'NDT Boreal',\n", + " 'BET Tropical', 'BET Temperate', 'BDT Tropical',\n", + " 'BDT Temperate', 'BDT Boreal', 'BES Temperate',\n", + " 'BDS Temperate', 'BDS Boreal', 'C3 Grass Arctic',\n", + " 'C3 Grass', 'C4 Grass', 'UCrop UIrr']\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "55ceea85-e3e2-4e2e-a88c-03c1313acf31", + "metadata": {}, + "outputs": [], + "source": [ + "# TODO Add this to ADF plotting functions?\n", + "def plot_ux_survival(uxdsOut, case=case, var='TLAI', npft = 15,\n", + " pft_names = pft_names, min_pft_wgt = 0.05):\n", + " '''\n", + " Accepts reshaped h1 file from unstructured grid\n", + " - Also requires case name, variable, and PFT names (strings), and\n", + " - min value for PFT weights (fraction of grid cell) as a mask\n", + " Currently hard coded to make nice PFT survival plots, but \n", + " Could be adapted for more general subgid output\n", + " '''\n", + "\n", + " # Basic plot settings\n", + " transform = ccrs.PlateCarree()\n", + " proj = ccrs.PlateCarree()\n", + " cmap = plt.cm.viridis_r\n", + " cmap.set_under(color='deeppink')\n", + " cmap = cmap.resampled(7)\n", + " levels = [0.1, 1, 2, 3, 4, 5, 6, 7]\n", + "\n", + " # create figure object\n", + " fig, axs = plt.subplots(5,3,\n", + " facecolor=\"w\",\n", + " constrained_layout=True,\n", + " subplot_kw=dict(projection=proj),\n", + " dpi=300)\n", + "\n", + " axs=axs.flatten()\n", + "\n", + " # Loop over pfts\n", + " for i in range((npft-1)):\n", + " # Calculate weights bases on area, landfrac and min_pft_wgt\n", + " pft_wgt = uxdsOut.pfts1d_wtgcell.isel(npft=i)\n", + " pft_wgt = pft_wgt.where(pft_wgt >= min_pft_wgt)\n", + " wgts = uxdsOut.area * uxdsOut.landfrac * pft_wgt\n", + " wgts = wgts / wgts.sum()\n", + "\n", + " # Plots where LAI > min_wgt on grid\n", + " axs[i].set_global()\n", + " plot_var = uxdsOut[var].isel(npft=i).where(pft_wgt >= min_pft_wgt)\n", + " raster = plot_var.to_raster(ax=axs[i])\n", + " img = axs[i].imshow(\n", + " raster, cmap=cmap, origin=\"lower\", extent=axs[i].get_xlim() + axs[i].get_ylim()\n", + " )\n", + " img.set_clim(vmin=0.1,vmax=6.9)\n", + "\n", + " # Add titles (pft names) & statistics (mean LAI & survival)\n", + " mean = str(np.round((uxdsOut[var].isel(npft=i)*wgts).sum().values,2))\n", + " dead = ((uxdsOut[var].isel(npft=i)<0.1)*wgts).sum()\n", + " live = ((uxdsOut[var].isel(npft=i)>0.1)*wgts).sum()\n", + " livefrac = str(np.round((live/(live+dead)).values,2))\n", + " axs[i].set_title(str(pft_names[(1+i)].data)[12:50], loc='left',size=6)\n", + " axs[i].text(-30, -45,'mean = '+ mean, fontsize=5)\n", + " axs[i].text(-45, -60,'live frac = '+livefrac,fontsize=5)\n", + "\n", + " # make panels look nice\n", + " for a in axs:\n", + " a.coastlines().set_linewidth(0.1)\n", + " a.set_global()\n", + " a.spines['geo'].set_linewidth(0.1) #cartopy's recommended method\n", + " a.set_extent([-180, 180, -65, 86])\n", + "\n", + " # add color bar with case name\n", + " fig.set_layout_engine(\"compressed\")\n", + " cbar_ax = fig.add_axes([0.94, 0.06, 0.02, 0.88])\n", + " cbar = fig.colorbar(img, cax=cbar_ax, pad=0.1, shrink=0.7, aspect=40, extend='both')\n", + " cbar.ax.tick_params(labelsize=9)\n", + " cbar.set_label(label=(\"max LAI \"+case), size=9, weight='bold')\n", + "\n", + " return fig\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1d2c3658-48a9-4ca9-931d-a592c46e1c60", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-- wrote pft TLAI figure --\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 32.1 s, sys: 115 ms, total: 32.2 s\n", + "Wall time: 39.7 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# Call plotting function\n", + "fig = plot_ux_survival (uxdsOut = uxdsOut, case = case, var = var,\n", + " pft_names = pft_names, min_pft_wgt = 0.05)\n", + "save = True\n", + "# TODO add ADF plotting and webpage hooks\n", + "if (save == True):\n", + " fig.savefig('h1_test_raster', bbox_inches='tight', dpi=300)\n", + " print('-- wrote pft '+var+' figure --')\n", + "plt.show() ;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1ef215e-562c-4702-a6a4-73389e9c7d2b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93c009b2-f94f-49d5-8810-16c44a3f4c92", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:cupid-analysis]", + "language": "python", + "name": "conda-env-cupid-analysis-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/lib/plotting_functions.py b/lib/plotting_functions.py index 08de58860..c48ac0c34 100644 --- a/lib/plotting_functions.py +++ b/lib/plotting_functions.py @@ -99,6 +99,7 @@ from mpl_toolkits.axes_grid1.inset_locator import inset_axes from matplotlib.lines import Line2D import matplotlib.cm as cm +import uxarray as ux #need npl 2024a or later from adf_diag import AdfDiag from adf_base import AdfError @@ -161,6 +162,38 @@ def load_dataset(fils): #End if #End def +def load_ux_dataset(fils, mesh=None): + """ + This method exists to get an uxarray Dataset from input file information that can be passed into the plotting methods. + + Parameters + ---------- + fils : list + strings or paths to input file(s) + + Returns + ------- + ux.UxDataArray + + Notes + ----- + When just one entry is provided, use `open_dataset`, otherwise `open_mfdatset` + """ + if mesh == None: + mesh = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc' + warnings.warn(f"No mesh file provided, using defaults ne30pg3 mesh file") + + if len(fils) == 0: + warnings.warn(f"Input file list is empty.") + return None + elif len(fils) > 1: + return ux.open_mfdataset(mesh, fils) + else: + return ux.open_dataset(mesh, fils[0]) + #End if +#End def + + def use_this_norm(): """Just use the right normalization; avoids a deprecation warning.""" @@ -408,6 +441,57 @@ def spatial_average(indata, weights=None, spatial_dims=None): return weighted.mean(dim=spatial_dims, keep_attrs=True) +# TODO, maybe just adapt the spatial average above? +# TODO, should there be some unit conversions for this defined in a variable dictionary? +def spatial_average_lnd(indata, weights, spatial_dims=None): + """Compute spatial average. + + Parameters + ---------- + indata : xr.DataArray + input data + weights xr.DataArray + weights (area * landfrac) + spatial_dims : list, optional + list of dimensions to average, see Notes for default behavior + + Returns + ------- + xr.DataArray + weighted average of `indata` + + Notes + ----- + weights are required + + Makes an attempt to identify the spatial variables when `spatial_dims` is None. + Will average over `ncol` if present, and then will check for `lat` and `lon`. + When none of those three are found, raise an AdfError. + """ + import warnings + + #Apply weights to input data: + weighted = indata*weights + + # we want to average over all non-time dimensions + if spatial_dims is None: + if 'lndgrid' in indata.dims: + spatial_dims = ['lndgrid'] + else: + spatial_dims = [dimname for dimname in indata.dims if (('lat' in dimname.lower()) or + ('lon' in dimname.lower()))] + if not spatial_dims: + #Scripts using this function likely expect the horizontal dimensions + #to be removed via the application of the mean. So in order to avoid + #possibly unexpected behavior due to arrays being incorrectly dimensioned + #(which could be difficult to debug) the ADF should die here: + emsg = "spatial_average: No spatial dimensions were identified," + emsg += " so can not perform average." + raise AdfError(emsg) + + + return weighted.sum(dim=spatial_dims, keep_attrs=True) + def wgt_rmse(fld1, fld2, wgt): """Calculate the area-weighted RMSE. @@ -429,7 +513,8 @@ def wgt_rmse(fld1, fld2, wgt): Notes: ```rmse = sqrt( mean( (fld1 - fld2)**2 ) )``` """ - assert len(fld1.shape) == 2, "Input fields must have exactly two dimensions." + wgt.fillna(0) + assert len(fld1.shape) <= 2, "Input fields must have less than two dimensions." assert fld1.shape == fld2.shape, "Input fields must have the same array shape." # in case these fields are in dask arrays, compute them now. if hasattr(fld1, "compute"): @@ -453,7 +538,7 @@ def wgt_rmse(fld1, fld2, wgt): ####### # Time-weighted averaging -def annual_mean(data, whole_years=False, time_name='time'): +def annual_mean(data, whole_years=False, time_name='time', use_ux=False): """Calculate annual averages from monthly time series data. Parameters @@ -490,7 +575,36 @@ def annual_mean(data, whole_years=False, time_name='time'): # this provides the normalized monthly weights in each year # -- do it for each year to allow for non-standard calendars (360-day) # -- and also to provision for data with leap years - days_gb = data_to_avg.time.dt.daysinmonth.groupby('time.year').map(lambda x: x / x.sum()) + days_in_month = data_to_avg.time.dt.daysinmonth + #print("days_in_month",days_in_month,'\n') + if not use_ux: + days_gb = data_to_avg.time.dt.daysinmonth.groupby('time.year').map(lambda x: x / x.sum()) + else: + # Group by the 'year' dimension + grouped_by_year = days_in_month.groupby('time.year') + + # Initialize a list to store the normalized days for each year + normalized_days = [] + + # Loop over each group and normalize the values (divide by the sum of the group) + for i, (year, group) in enumerate(grouped_by_year): + # Compute the sum of days in the month for the current year + print(group) + year_sum = group[12*i:12*i+12].sum() + + # Normalize the group by dividing each value by the sum of the group + normalized_group = group[12*i:12*i+12] / year_sum + + # Append the normalized values to the list + normalized_days.append(normalized_group) + + # Concatenate the normalized days back together (align them with the original data) + days_gb = xr.concat(normalized_days, dim='time') + + # Alternatively, if you want to make sure the result has the same coordinates as the original + days_gb = days_in_month.copy(data=np.concatenate([g.values for g in normalized_days])) + days_gb.coords['time'] = days_in_month.coords['time'] # Reassign the correct time coordinates + # weighted average with normalized weights: = SUM x_i * w_i (implied division by SUM w_i) result = (data_to_avg * days_gb).groupby('time.year').sum(dim='time') result.attrs['averaging_period'] = date_range_string @@ -545,6 +659,12 @@ def seasonal_mean(data, season=None, is_climo=None): if "month" in data.dims: data = data.rename({"month":"time"}) has_time = True + if isinstance(data, ux.UxDataset): + has_time = 'time' in data.dims + if not has_time: + if "month" in data.dims: + data = data.rename({"month":"time"}) + has_time = True if not has_time: # this might happen if a pure numpy array gets passed in # --> assumes ordered January to December. @@ -567,7 +687,7 @@ def seasonal_mean(data, season=None, is_climo=None): #Polar Plot functions -def domain_stats(data, domain): +def domain_stats(data, domain, unstructured=False): """Provides statistics in specified region. Parameters @@ -597,16 +717,22 @@ def domain_stats(data, domain): spatial_average """ - x_region = data.sel(lat=slice(domain[2],domain[3]), lon=slice(domain[0],domain[1])) - x_region_mean = x_region.weighted(np.cos(np.deg2rad(x_region['lat']))).mean().item() + if not unstructured: + x_region = data.sel(lat=slice(domain[2],domain[3]), lon=slice(domain[0],domain[1])) + x_region_mean = x_region.weighted(np.cos(np.deg2rad(x_region['lat']))).mean().item() + else: + x_region = data + x_region_mean = data.mean().item() x_region_min = x_region.min().item() x_region_max = x_region.max().item() return x_region_mean, x_region_max, x_region_min + + def make_polar_plot(wks, case_nickname, base_nickname, case_climo_yrs, baseline_climo_yrs, - d1:xr.DataArray, d2:xr.DataArray, difference:Optional[xr.DataArray]=None,pctchange:Optional[xr.DataArray]=None, - domain:Optional[list]=None, hemisphere:Optional[str]=None, obs=False, **kwargs): + d1, d2, difference=None,pctchange=None, + domain:Optional[list]=None, hemisphere:Optional[str]=None, obs=False, unstructured=False, **kwargs): """Make a stereographic polar plot for the given data and hemisphere. @@ -659,10 +785,15 @@ def make_polar_plot(wks, case_nickname, base_nickname, pct = pctchange #check if pct has NaN's or Inf values and if so set them to 0 to prevent plotting errors - pct = pct.where(np.isfinite(pct), np.nan) - pct = pct.fillna(0.0) + pct_0 = pct.where(np.isfinite(pct), np.nan) + pct_0 = pct_0.fillna(0.0) + if isinstance(pct, ux.UxDataArray): + pct_grid = pct.uxgrid + pct = ux.UxDataArray(pct_0,uxgrid=pct_grid) + else: + pct = pct_0 - if hemisphere.upper() == "NH": + if (hemisphere.upper() == "NH") or (hemisphere == "Arctic"): proj = ccrs.NorthPolarStereo() elif hemisphere.upper() == "SH": proj = ccrs.SouthPolarStereo() @@ -672,26 +803,54 @@ def make_polar_plot(wks, case_nickname, base_nickname, if domain is None: if hemisphere.upper() == "NH": domain = [-180, 180, 45, 90] + if hemisphere == "Arctic": + domain = [-180, 180, 50, 90] else: domain = [-180, 180, -90, -45] - # statistics for annotation (these are scalars): + """# statistics for annotation (these are scalars): d1_region_mean, d1_region_max, d1_region_min = domain_stats(d1, domain) d2_region_mean, d2_region_max, d2_region_min = domain_stats(d2, domain) dif_region_mean, dif_region_max, dif_region_min = domain_stats(dif, domain) - pct_region_mean, pct_region_max, pct_region_min = domain_stats(pct, domain) + pct_region_mean, pct_region_max, pct_region_min = domain_stats(pct, domain)""" + means = [] + mins = [] + maxs = [] + if not unstructured: + # statistics for annotation (these are scalars): + d1_region_mean, d1_region_max, d1_region_min = domain_stats(d1, domain) + d2_region_mean, d2_region_max, d2_region_min = domain_stats(d2, domain) + dif_region_mean, dif_region_max, dif_region_min = domain_stats(dif, domain) + pct_region_mean, pct_region_max, pct_region_min = domain_stats(pct, domain) + #downsize to the specified region; makes plotting/rendering/saving much faster + d1 = d1.sel(lat=slice(domain[2],domain[3])) + d2 = d2.sel(lat=slice(domain[2],domain[3])) + dif = dif.sel(lat=slice(domain[2],domain[3])) + pct = pct.sel(lat=slice(domain[2],domain[3])) + + # add cyclic point to the data for better-looking plot + d1_cyclic, lon_cyclic = add_cyclic_point(d1, coord=d1.lon) + d2_cyclic, _ = add_cyclic_point(d2, coord=d2.lon) # since we can take difference, assume same longitude coord. + dif_cyclic, _ = add_cyclic_point(dif, coord=dif.lon) + pct_cyclic, _ = add_cyclic_point(pct, coord=pct.lon) + #wrap_fields = (d1_cyclic, d2_cyclic, dif_cyclic, pct_cyclic) + wrap_fields = (d1_cyclic, d2_cyclic, pct_cyclic, dif_cyclic) + lons, lats = np.meshgrid(lon_cyclic, d1.lat) + else: + wgt = kwargs["wgt"] + #wrap_fields = (d1, d2, dif, pct) + wrap_fields = (d1, d2, pct, dif) + area_avg = [global_average(x, wgt) for x in wrap_fields] - #downsize to the specified region; makes plotting/rendering/saving much faster - d1 = d1.sel(lat=slice(domain[2],domain[3])) - d2 = d2.sel(lat=slice(domain[2],domain[3])) - dif = dif.sel(lat=slice(domain[2],domain[3])) - pct = pct.sel(lat=slice(domain[2],domain[3])) + d1_region_mean, d1_region_max, d1_region_min = domain_stats(d1, domain, unstructured) + d2_region_mean, d2_region_max, d2_region_min = domain_stats(d2, domain, unstructured) + dif_region_mean, dif_region_max, dif_region_min = domain_stats(dif, domain, unstructured) + pct_region_mean, pct_region_max, pct_region_min = domain_stats(pct, domain, unstructured) - # add cyclic point to the data for better-looking plot - d1_cyclic, lon_cyclic = add_cyclic_point(d1, coord=d1.lon) - d2_cyclic, _ = add_cyclic_point(d2, coord=d2.lon) # since we can take difference, assume same longitude coord. - dif_cyclic, _ = add_cyclic_point(dif, coord=dif.lon) - pct_cyclic, _ = add_cyclic_point(pct, coord=pct.lon) + + # TODO Check this is correct, weighted rmse uses xarray weighted function + #d_rmse = wgt_rmse(a, b, wgt) + d_rmse = (np.sqrt(((dif**2)*wgt).sum())).values.item() # -- deal with optional plotting arguments that might provide variable-dependent choices @@ -701,200 +860,151 @@ def make_polar_plot(wks, case_nickname, base_nickname, absmaxdif = np.max(np.abs(dif)) absmaxpct = np.max(np.abs(pct)) - if 'colormap' in kwargs: - cmap1 = kwargs['colormap'] - else: - cmap1 = 'coolwarm' - - if 'contour_levels' in kwargs: - levels1 = kwargs['contour_levels'] - norm1 = mpl.colors.Normalize(vmin=min(levels1), vmax=max(levels1)) - elif 'contour_levels_range' in kwargs: - assert len(kwargs['contour_levels_range']) == 3, "contour_levels_range must have exactly three entries: min, max, step" - levels1 = np.arange(*kwargs['contour_levels_range']) - norm1 = mpl.colors.Normalize(vmin=min(levels1), vmax=max(levels1)) - else: - levels1 = np.linspace(minval, maxval, 12) - norm1 = mpl.colors.Normalize(vmin=minval, vmax=maxval) - - if ('colormap' not in kwargs) and ('contour_levels' not in kwargs): - norm1, cmap1 = get_difference_colors(levels1) # maybe these are better defaults if nothing else is known. - - if "diff_contour_levels" in kwargs: - levelsdiff = kwargs["diff_contour_levels"] # a list of explicit contour levels - elif "diff_contour_range" in kwargs: - assert len(kwargs['diff_contour_range']) == 3, "diff_contour_range must have exactly three entries: min, max, step" - levelsdiff = np.arange(*kwargs['diff_contour_range']) - else: - # set levels for difference plot (with a symmetric color bar): - levelsdiff = np.linspace(-1*absmaxdif, absmaxdif, 12) - #End if - - if "pct_diff_contour_levels" in kwargs: - levelspctdiff = kwargs["pct_diff_contour_levels"] # a list of explicit contour levels - elif "pct_diff_contour_range" in kwargs: - assert len(kwargs['pct_diff_contour_range']) == 3, "pct_diff_contour_range must have exactly three entries: min, max, step" - levelspctdiff = np.arange(*kwargs['pct_diff_contour_range']) - else: - levelspctdiff = [-100,-75,-50,-40,-30,-20,-10,-8,-6,-4,-2,0,2,4,6,8,10,20,30,40,50,75,100] - pctnorm = mpl.colors.BoundaryNorm(levelspctdiff,256) - - #NOTE: Sometimes the contour levels chosen in the defaults file - #can result in the "contourf" software stack generating a - #'TypologyException', which should manifest itself as a - #"PredicateError", but due to bugs in the stack itself - #will also sometimes raise an AttributeError. - - #To prevent this from happening, the polar max and min values - #are calculated, and if the default contour values are significantly - #larger then the min-max values, then the min-max values are used instead: - #------------------------------- - if max(levels1) > 10*maxval: - levels1 = np.linspace(minval, maxval, 12) - norm1 = mpl.colors.Normalize(vmin=minval, vmax=maxval) - elif minval < 0 and min(levels1) < 10*minval: - levels1 = np.linspace(minval, maxval, 12) - norm1 = mpl.colors.Normalize(vmin=minval, vmax=maxval) - #End if - - if max(np.abs(levelsdiff)) > 10*absmaxdif: - levelsdiff = np.linspace(-1*absmaxdif, absmaxdif, 12) - - - #End if - #------------------------------- - - # Difference options -- Check in kwargs for colormap and levels - if "diff_colormap" in kwargs: - cmapdiff = kwargs["diff_colormap"] - dnorm, _ = get_difference_colors(levelsdiff) # color map output ignored - else: - dnorm, cmapdiff = get_difference_colors(levelsdiff) - - # Pct Difference options -- Check in kwargs for colormap and levels - if "pct_diff_colormap" in kwargs: - cmappct = kwargs["pct_diff_colormap"] - else: - cmappct = "PuOr_r" - #End if + means.extend([d1_region_mean,d2_region_mean, pct_region_mean, dif_region_mean]) + mins.extend([d1_region_min,d2_region_min, pct_region_min, dif_region_min]) + maxs.extend([d1_region_max,d2_region_max, pct_region_max, dif_region_max]) # -- end options - lons, lats = np.meshgrid(lon_cyclic, d1.lat) + #lons, lats = np.meshgrid(lon_cyclic, d1.lat) - fig = plt.figure(figsize=(10,10)) - gs = mpl.gridspec.GridSpec(2, 4, wspace=0.9) + # controling DPI makes uxplots look better + fig = plt.figure(figsize=(10,10), dpi=300) + gs = mpl.gridspec.GridSpec(2, 4, wspace=0.6) ax1 = plt.subplot(gs[0, :2], projection=proj) ax2 = plt.subplot(gs[0, 2:], projection=proj) ax3 = plt.subplot(gs[1, :2], projection=proj) ax4 = plt.subplot(gs[1, 2:], projection=proj) + axs = [ax1,ax2,ax3,ax4] - levs = np.unique(np.array(levels1)) - levs_diff = np.unique(np.array(levelsdiff)) - levs_pctdiff = np.unique(np.array(levelspctdiff)) + #generate a dictionary of contour plot settings: + cp_info = prep_contour_plot(d1, d2, pct, dif, **kwargs) - if len(levs) < 2: - img1 = ax1.contourf(lons, lats, d1_cyclic, transform=ccrs.PlateCarree(), colors="w", norm=norm1) - ax1.text(0.4, 0.4, empty_message, transform=ax1.transAxes, bbox=props) + imgs = [] - img2 = ax2.contourf(lons, lats, d2_cyclic, transform=ccrs.PlateCarree(), colors="w", norm=norm1) - ax2.text(0.4, 0.4, empty_message, transform=ax2.transAxes, bbox=props) - else: - img1 = ax1.contourf(lons, lats, d1_cyclic, transform=ccrs.PlateCarree(), cmap=cmap1, norm=norm1, levels=levels1) - img2 = ax2.contourf(lons, lats, d2_cyclic, transform=ccrs.PlateCarree(), cmap=cmap1, norm=norm1, levels=levels1) - - if len(levs_pctdiff) < 2: - img3 = ax3.contourf(lons, lats, pct_cyclic, transform=ccrs.PlateCarree(), colors="w", norm=pctnorm, transform_first=True) - ax3.text(0.4, 0.4, empty_message, transform=ax3.transAxes, bbox=props) - else: - img3 = ax3.contourf(lons, lats, pct_cyclic, transform=ccrs.PlateCarree(), cmap=cmappct, norm=pctnorm, levels=levelspctdiff, transform_first=True) + # Loop over data arrays to make plots + for i, a in enumerate(wrap_fields): + if i == len(wrap_fields)-1: + levels = cp_info['levelsdiff'] + cmap = cp_info['cmapdiff'] + norm = cp_info['normdiff'] + elif i == len(wrap_fields)-2: + levels = cp_info['levelspctdiff'] + cmap = cp_info['cmappct'] + norm = cp_info['pctnorm'] + else: + levels = cp_info['levels1'] + cmap = cp_info['cmap1'] + norm = cp_info['norm1'] + if unstructured: + #configure for polycollection plotting + # TODO, would be nice to have levels set from the info, above + # raster approach should be faster + axs[i].set_global() + raster = a.to_raster(ax=axs[i]) + img = axs[i].imshow( + raster, cmap=cmap, origin="lower", extent=axs[i].get_xlim() + axs[i].get_ylim() + ) + img.set_clim(vmin=levels[0],vmax=levels[-1]) + imgs.append(img) - if len(levs_diff) < 2: - img4 = ax4.contourf(lons, lats, dif_cyclic, transform=ccrs.PlateCarree(), colors="w", norm=dnorm) - ax4.text(0.4, 0.4, empty_message, transform=ax4.transAxes, bbox=props) - else: - img4 = ax4.contourf(lons, lats, dif_cyclic, transform=ccrs.PlateCarree(), cmap=cmapdiff, norm=dnorm, levels=levelsdiff) - + else: + + levs = np.unique(np.array(levels)) + if len(levs) < 2: + imgs.append(axs[i].contourf(lons,lats,a,colors="w",transform=ccrs.PlateCarree(),transform_first=True)) + axs[i].text(0.4, 0.4, empty_message, transform=axs[i].transAxes, bbox=props) + else: + imgs.append(axs[i].contourf(lons, lats, a, levels=levels, cmap=cmap, norm=norm, + transform=ccrs.PlateCarree(), #transform_first=True, + **cp_info['contourf_opt'])) + #End if + #End if unstructured + + # Set stats for title + stat_mean = f"Mean: {means[i]:5.2f}" + stat_max = f"Max: {maxs[i]:5.2f}" + stat_min = f"Min: {mins[i]:5.2f}" + stats = f"{stat_mean}\n{stat_max}\n{stat_min}" + axs[i].set_title(stats, loc='right', fontsize=8) + #End for + #Set Main title for subplots: st = fig.suptitle(wks.stem[:-5].replace("_"," - "), fontsize=18) st.set_y(0.95) #Set plot titles case_title = "$\mathbf{Test}:$"+f"{case_nickname}\nyears: {case_climo_yrs[0]}-{case_climo_yrs[-1]}" - ax1.set_title(case_title, loc='left', fontsize=6) #fontsize=tiFontSize + axs[0].set_title(case_title, loc='left', fontsize=8) #fontsize=tiFontSize if obs: obs_var = kwargs["obs_var_name"] obs_title = kwargs["obs_file"][:-3] base_title = "$\mathbf{Baseline}:$"+obs_title+"\n"+"$\mathbf{Variable}:$"+f"{obs_var}" - ax2.set_title(base_title, loc='left', fontsize=6) #fontsize=tiFontSize + axs[1].set_title(base_title, loc='left', fontsize=8) #fontsize=tiFontSize else: base_title = "$\mathbf{Baseline}:$"+f"{base_nickname}\nyears: {baseline_climo_yrs[0]}-{baseline_climo_yrs[-1]}" - ax2.set_title(base_title, loc='left', fontsize=6) + axs[1].set_title(base_title, loc='left', fontsize=8) - ax1.text(-0.2, -0.10, f"Mean: {d1_region_mean:5.2f}\nMax: {d1_region_max:5.2f}\nMin: {d1_region_min:5.2f}", transform=ax1.transAxes) - - ax2.text(-0.2, -0.10, f"Mean: {d2_region_mean:5.2f}\nMax: {d2_region_max:5.2f}\nMin: {d2_region_min:5.2f}", transform=ax2.transAxes) - - ax3.text(-0.2, -0.10, f"Mean: {pct_region_mean:5.2f}\nMax: {pct_region_max:5.2f}\nMin: {pct_region_min:5.2f}", transform=ax3.transAxes) - ax3.set_title("Test % diff Baseline", loc='left', fontsize=8) - - ax4.text(-0.2, -0.10, f"Mean: {dif_region_mean:5.2f}\nMax: {dif_region_max:5.2f}\nMin: {dif_region_min:5.2f}", transform=ax4.transAxes) - ax4.set_title("$\mathbf{Test} - \mathbf{Baseline}$", loc='left', fontsize=8) + axs[2].set_title("Test % Diff Baseline", loc='left', fontsize=8,fontweight="bold") + axs[3].set_title("$\mathbf{Test} - \mathbf{Baseline}$", loc='left', fontsize=8) if "units" in kwargs: - ax2.set_ylabel(kwargs["units"]) - ax4.set_ylabel(kwargs["units"]) + axs[1].set_ylabel(kwargs["units"]) + axs[3].set_ylabel(kwargs["units"]) else: - ax2.set_ylabel(f"{d1.units}") - ax4.set_ylabel(f"{d1.units}") - - [a.set_extent(domain, ccrs.PlateCarree()) for a in [ax1, ax2, ax3, ax4]] - [a.coastlines() for a in [ax1, ax2, ax3, ax4]] - - # __Follow the cartopy gallery example to make circular__: - # Compute a circle in axes coordinates, which we can use as a boundary - # for the map. We can pan/zoom as much as we like - the boundary will be - # permanently circular. - theta = np.linspace(0, 2*np.pi, 100) - center, radius = [0.5, 0.5], 0.5 - verts = np.vstack([np.sin(theta), np.cos(theta)]).T - circle = mpl.path.Path(verts * radius + center) - [a.set_boundary(circle, transform=a.transAxes) for a in [ax1, ax2, ax3, ax4]] + axs[1].set_ylabel(f"{d1.units}") + axs[3].set_ylabel(f"{d1.units}") + + for a in axs: + a.coastlines() + a.set_extent(domain, ccrs.PlateCarree()) + # __Follow the cartopy gallery example to make circular__: + # Compute a circle in axes coordinates, which we can use as a boundary + # for the map. We can pan/zoom as much as we like - the boundary will be + # permanently circular. + theta = np.linspace(0, 2*np.pi, 100) + center, radius = [0.5, 0.5], 0.5 + verts = np.vstack([np.sin(theta), np.cos(theta)]).T + circle = mpl.path.Path(verts * radius + center) + a.set_boundary(circle, transform=a.transAxes) + a.gridlines(draw_labels=False, crs=ccrs.PlateCarree(), + lw=1, color="gray",y_inline=True, + xlocs=range(-180,180,90), ylocs=range(0,90,10)) # __COLORBARS__ - cb_mean_ax = inset_axes(ax2, + cb_mean_ax = inset_axes(axs[1], width="5%", # width = 5% of parent_bbox width height="90%", # height : 90% loc='lower left', bbox_to_anchor=(1.05, 0.05, 1, 1), - bbox_transform=ax2.transAxes, + bbox_transform=axs[1].transAxes, borderpad=0, ) - fig.colorbar(img1, cax=cb_mean_ax) + fig.colorbar(imgs[0], cax=cb_mean_ax) - cb_pct_ax = inset_axes(ax3, + cb_pct_ax = inset_axes(axs[3], width="5%", # width = 5% of parent_bbox width height="90%", # height : 90% loc='lower left', bbox_to_anchor=(1.05, 0.05, 1, 1), - bbox_transform=ax3.transAxes, + bbox_transform=axs[3].transAxes, borderpad=0, ) - cb_diff_ax = inset_axes(ax4, + cb_diff_ax = inset_axes(axs[2], width="5%", # width = 5% of parent_bbox width height="90%", # height : 90% loc='lower left', bbox_to_anchor=(1.05, 0.05, 1, 1), - bbox_transform=ax4.transAxes, + bbox_transform=axs[2].transAxes, borderpad=0, ) - fig.colorbar(img3, cax=cb_pct_ax) + fig.colorbar(imgs[3], cax=cb_pct_ax) - fig.colorbar(img4, cax=cb_diff_ax) + fig.colorbar(imgs[2], cax=cb_diff_ax) # Save files fig.savefig(wks, bbox_inches='tight', dpi=300) @@ -904,6 +1014,7 @@ def make_polar_plot(wks, case_nickname, base_nickname, ####### + def plot_map_vect_and_save(wks, case_nickname, base_nickname, case_climo_yrs, baseline_climo_yrs, plev, umdlfld_nowrap, vmdlfld_nowrap, @@ -1143,9 +1254,11 @@ def plot_map_vect_and_save(wks, case_nickname, base_nickname, ####### + def plot_map_and_save(wks, case_nickname, base_nickname, case_climo_yrs, baseline_climo_yrs, - mdlfld, obsfld, diffld, pctld, obs=False, **kwargs): + mdlfld, obsfld, diffld, pctld, unstructured=False, + obs=False, **kwargs): """This plots mdlfld, obsfld, diffld in a 3-row panel plot of maps. Parameters @@ -1203,24 +1316,35 @@ def plot_map_and_save(wks, case_nickname, base_nickname, from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter # preprocess - # - assume all three fields have same lat/lon - lat = obsfld['lat'] - wgt = np.cos(np.radians(lat)) - mwrap, lon = add_cyclic_point(mdlfld, coord=mdlfld['lon']) - owrap, _ = add_cyclic_point(obsfld, coord=obsfld['lon']) - dwrap, _ = add_cyclic_point(diffld, coord=diffld['lon']) - pwrap, _ = add_cyclic_point(pctld, coord=pctld['lon']) - wrap_fields = (mwrap, owrap, pwrap, dwrap) - # mesh for plots: - lons, lats = np.meshgrid(lon, lat) - # Note: using wrapped data makes spurious lines across plot (maybe coordinate dependent) - lon2, lat2 = np.meshgrid(mdlfld['lon'], mdlfld['lat']) - - # get statistics (from non-wrapped) - fields = (mdlfld, obsfld, diffld, pctld) - area_avg = [spatial_average(x, weights=wgt, spatial_dims=None) for x in fields] + if not unstructured: + # - assume all three fields have same lat/lon + lat = obsfld['lat'] + wgt = np.cos(np.radians(lat)) + mwrap, lon = add_cyclic_point(mdlfld, coord=mdlfld['lon']) + owrap, _ = add_cyclic_point(obsfld, coord=obsfld['lon']) + dwrap, _ = add_cyclic_point(diffld, coord=diffld['lon']) + pwrap, _ = add_cyclic_point(pctld, coord=pctld['lon']) + wrap_fields = (mwrap, owrap, pwrap, dwrap) + # mesh for plots: + lons, lats = np.meshgrid(lon, lat) + # Note: using wrapped data makes spurious lines across plot (maybe coordinate dependent) + lon2, lat2 = np.meshgrid(mdlfld['lon'], mdlfld['lat']) + + # get statistics (from non-wrapped) + fields = (mdlfld, obsfld, pctld, diffld) + area_avg = [spatial_average(x, weights=wgt, spatial_dims=None) for x in fields] + + d_rmse = wgt_rmse(mdlfld, obsfld, wgt) # correct weighted RMSE for (lat,lon) fields. + # specify the central longitude for the plot + central_longitude = kwargs.get('central_longitude', 180) + else: + wgt = kwargs["wgt"] + wrap_fields = (mdlfld, obsfld, pctld, diffld) + area_avg = [global_average(x, wgt) for x in wrap_fields] - d_rmse = wgt_rmse(mdlfld, obsfld, wgt) # correct weighted RMSE for (lat,lon) fields. + # TODO Check this is correct, weighted rmse uses xarray weighted function + #d_rmse = wgt_rmse(a, b, wgt) + d_rmse = (np.sqrt(((diffld**2)*wgt).sum())).values.item() # We should think about how to do plot customization and defaults. # Here I'll just pop off a few custom ones, and then pass the rest into mpl. @@ -1236,14 +1360,14 @@ def plot_map_and_save(wks, case_nickname, base_nickname, tiFontSize = 8 #End if + central_longitude = kwargs.get('central_longitude', 180) + # generate dictionary of contour plot settings: cp_info = prep_contour_plot(mdlfld, obsfld, diffld, pctld, **kwargs) - # specify the central longitude for the plot - central_longitude = kwargs.get('central_longitude', 180) - - # create figure object - fig = plt.figure(figsize=(14,10)) + # create figure object, + # controling DPI improves raster plots for unstructured data, but it does slow things down + fig = plt.figure(figsize=(14,8), dpi=300) # LAYOUT WITH GRIDSPEC gs = mpl.gridspec.GridSpec(3, 6, wspace=2.0,hspace=0.0) # 2 rows, 4 columns, but each map will take up 2 columns @@ -1255,8 +1379,8 @@ def plot_map_and_save(wks, case_nickname, base_nickname, ax = [ax1,ax2,ax3,ax4] img = [] # contour plots - cs = [] # contour lines - cb = [] # color bars + cs = [] # contour lines, unused for now + cb = [] # color bars, unused for now # formatting for tick labels lon_formatter = LongitudeFormatter(number_format='0.0f', @@ -1279,15 +1403,36 @@ def plot_map_and_save(wks, case_nickname, base_nickname, levels = cp_info['levels1'] cmap = cp_info['cmap1'] norm = cp_info['norm1'] - - levs = np.unique(np.array(levels)) - if len(levs) < 2: - img.append(ax[i].contourf(lons,lats,a,colors="w",transform=ccrs.PlateCarree(),transform_first=True)) - ax[i].text(0.4, 0.4, empty_message, transform=ax[i].transAxes, bbox=props) + + # Unstructured grid check + if not unstructured: + levs = np.unique(np.array(levels)) + if len(levs) < 2: + img.append(ax[i].contourf(lons,lats,a,colors="w",transform=ccrs.PlateCarree(), + transform_first=True)) + ax[i].text(0.4, 0.4, empty_message, transform=ax[i].transAxes, bbox=props) + else: + img.append(ax[i].contourf(lons, lats, a, levels=levels, cmap=cmap, norm=norm, + transform=ccrs.PlateCarree(), transform_first=True, + **cp_info['contourf_opt'] + )) + #End if else: - img.append(ax[i].contourf(lons, lats, a, levels=levels, cmap=cmap, norm=norm, transform=ccrs.PlateCarree(), transform_first=True, **cp_info['contourf_opt'])) - #End if - ax[i].set_title("AVG: {0:.3f}".format(area_avg[i]), loc='right', fontsize=11) + #configure for raster plotting, polycollection was slower + #TODO, would be nice to have levels set from the info, above + ax[i].set_global() + raster = a.to_raster(ax=ax[i]) + im = ax[i].imshow( + raster, cmap=cmap, origin="lower", + extent=ax[i].get_xlim() + ax[i].get_ylim() + ) + im.set_clim(vmin=levels[0],vmax=levels[-1]) + img.append(im) + # End if unstructured grid + + #ax[i].set_title("AVG: {0:.3f}".format(area_avg[i]), loc='right', fontsize=11) + ax[i].set_title(f"Mean: {area_avg[i].item():5.2f}\nMax: {wrap_fields[i].max().item():5.2f}\nMin: {wrap_fields[i].min().item():5.2f}", + loc='right', fontsize=tiFontSize) # add contour lines <- Unused for now -JN # TODO: add an option to turn this on -BM @@ -1296,6 +1441,17 @@ def plot_map_and_save(wks, case_nickname, base_nickname, #ax[i].text( 10, -140, "CONTOUR FROM {} to {} by {}".format(min(cs[i].levels), max(cs[i].levels), cs[i].levels[1]-cs[i].levels[0]), #bbox=dict(facecolor='none', edgecolor='black'), fontsize=tiFontSize-2) + # Custom setting for each subplot + for a in ax: + a.coastlines() + a.set_global() + a.spines['geo'].set_linewidth(1.5) #cartopy's recommended method + a.set_xticks(np.linspace(-180, 120, 6), crs=proj) + a.set_yticks(np.linspace(-90, 90, 7), crs=proj) + a.tick_params('both', length=5, width=1.5, which='both') + a.xaxis.set_major_formatter(lon_formatter) + a.yaxis.set_major_formatter(lat_formatter) + st = fig.suptitle(wks.stem[:-5].replace("_"," - "), fontsize=18) st.set_y(0.85) @@ -1312,30 +1468,17 @@ def plot_map_and_save(wks, case_nickname, base_nickname, base_title = "$\mathbf{Baseline}:$"+f"{base_nickname}\nyears: {baseline_climo_yrs[0]}-{baseline_climo_yrs[-1]}" ax[1].set_title(base_title, loc='left', fontsize=tiFontSize) - #Set stats: area_avg - ax[0].set_title(f"Mean: {mdlfld.weighted(wgt).mean().item():5.2f}\nMax: {mdlfld.max():5.2f}\nMin: {mdlfld.min():5.2f}", loc='right', - fontsize=tiFontSize) - ax[1].set_title(f"Mean: {obsfld.weighted(wgt).mean().item():5.2f}\nMax: {obsfld.max():5.2f}\nMin: {obsfld.min():5.2f}", loc='right', - fontsize=tiFontSize) - ax[2].set_title(f"Mean: {pctld.weighted(wgt).mean().item():5.2f}\nMax: {pctld.max():5.2f}\nMin: {pctld.min():5.2f}", loc='right', - fontsize=tiFontSize) - ax[3].set_title(f"Mean: {diffld.weighted(wgt).mean().item():5.2f}\nMax: {diffld.max():5.2f}\nMin: {diffld.min():5.2f}", loc='right', - fontsize=tiFontSize) - # set rmse title: ax[3].set_title(f"RMSE: {d_rmse:.3f}", fontsize=tiFontSize) ax[3].set_title("$\mathbf{Test} - \mathbf{Baseline}$", loc='left', fontsize=tiFontSize) ax[2].set_title("Test % Diff Baseline", loc='left', fontsize=tiFontSize,fontweight="bold") - for a in ax: - a.spines['geo'].set_linewidth(1.5) #cartopy's recommended method - a.coastlines() - a.set_xticks(np.linspace(-180, 120, 6), crs=ccrs.PlateCarree()) - a.set_yticks(np.linspace(-90, 90, 7), crs=ccrs.PlateCarree()) - a.tick_params('both', length=5, width=1.5, which='major') - a.tick_params('both', length=5, width=1.5, which='minor') - a.xaxis.set_major_formatter(lon_formatter) - a.yaxis.set_major_formatter(lat_formatter) + # Cosmetic adjustments to avoid label overlap + # also makes plots different sizes... + #ax[0].set_xticklabels([]) + #ax[1].set_xticklabels([]) + #ax[1].set_yticklabels([]) + #ax[3].set_yticklabels([]) # __COLORBARS__ cb_mean_ax = inset_axes(ax2, @@ -1377,7 +1520,207 @@ def plot_map_and_save(wks, case_nickname, base_nickname, plt.close() -# +### END plot_map_and_save + +# I don't think this is used anywhere and could likely be removed -WW +def plot_unstructured_map_and_save(wks, case_nickname, base_nickname, + case_climo_yrs, baseline_climo_yrs, + mdlfld, obsfld, diffld, pctld, wgt, + obs=False, projection='global',**kwargs): + + """This plots mdlfld, obsfld, diffld in a 3-row panel plot of maps. + + Parameters + ---------- + wks : str or Path + output file path + case_nickname : str + short name for case + base_nickname : str + short name for base case + case_climo_yrs : list + list of years in case climatology, used for annotation + baseline_climo_yrs : list + list of years in base case climatology, used for annotation + mdlfld : uxarray.DataArray + input data for case, needs units and long name attrubutes + obsfld : uxarray.DataArray + input data for base case, needs units and long name attrubutes + diffld : uxarray.DataArray + input difference data, needs units and long name attrubutes + pctld : uxarray.DataArray + input percent difference data, needs units and long name attrubutes + wgt : uxarray.DataArray + weights assumed to be (area*landfrac)/(area*landfrac).sum() + kwargs : dict, optional + variable-specific options, See Notes + + Notes + ----- + kwargs expected to be a variable-specific section, + possibly provided by an ADF Variable Defaults YAML file. + Currently it is inspected for: + - colormap -> str, name of matplotlib colormap + - contour_levels -> list of explict values or a tuple: (min, max, step) + - diff_colormap + - diff_contour_levels + - tiString -> str, Title String + - tiFontSize -> int, Title Font Size + - mpl -> dict, This should be any matplotlib kwargs that should be passed along. Keep reading: + + Organize these by the mpl function. In this function (`plot_map_and_save`) + we will check for an entry called `subplots`, `contourf`, and `colorbar`. So the YAML might looks something like: + ``` + mpl: + subplots: + figsize: (3, 9) + contourf: + levels: 15 + cmap: Blues + colorbar: + shrink: 0.4 + ``` + + This is experimental, and if you find yourself doing much with this, you probably should write a new plotting script that does not rely on this module. + When these are not provided, colormap is set to 'coolwarm' and limits/levels are set by data range. + """ + + # prepare info for plotting + wrap_fields = (mdlfld, obsfld, diffld, pctld) + area_avg = [global_average(x, wgt) for x in wrap_fields] + + # TODO Check this is correct, weighted rmse uses xarray weighted function + #d_rmse = wgt_rmse(a, b, wgt) + d_rmse = (np.sqrt(((diffld**2)*wgt).sum())).values.item() + + # We should think about how to do plot customization and defaults. + # Here I'll just pop off a few custom ones, and then pass the rest into mpl. + if 'tiString' in kwargs: + tiString = kwargs.pop("tiString") + else: + tiString = '' + + if 'tiFontSize' in kwargs: + tiFontSize = kwargs.pop('tiFontSize') + else: + tiFontSize = 8 + + #generate a dictionary of contour plot settings: + cp_info = prep_contour_plot(mdlfld, obsfld, diffld, pctld, **kwargs) + + if projection == 'global': + transform = ccrs.PlateCarree() + proj = ccrs.PlateCarree() + figsize= (14, 7) + elif projection == 'arctic': + transform = ccrs.NorthPolarStereo() + proj = ccrs.NorthPolarStereo() + figsize = (8, 8) + + #nice formatting for tick labels + from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter + lon_formatter = LongitudeFormatter(number_format='0.0f', + degree_symbol='', + dateline_direction_label=False) + lat_formatter = LatitudeFormatter(number_format='0.0f', + degree_symbol='') + + # create figure object + fig, axs = plt.subplots(2,2, + figsize=figsize, + facecolor="w", + constrained_layout=True, + subplot_kw=dict(projection=proj), + dpi=300, + **cp_info['subplots_opt'] + ) + axs=axs.flatten() + + # Loop over data arrays to make plots + for i, a in enumerate(wrap_fields): + if i == len(wrap_fields)-2: + levels = cp_info['levelsdiff'] + cmap = cp_info['cmapdiff'] + norm = cp_info['normdiff'] + elif i == len(wrap_fields)-1: + levels = cp_info['levelspctdiff'] + cmap = cp_info['cmappct'] + norm = cp_info['pctnorm'] + else: + levels = cp_info['levels1'] + cmap = cp_info['cmap1'] + norm = cp_info['norm1'] + + levs = np.unique(np.array(levels)) + + #configure for polycollection plotting + #TODO, would be nice to have levels set from the info, above + axs[i].set_global() + raster = a.to_raster(ax=axs[i]) + img = axs[i].imshow( + raster, cmap=cmap, origin="lower", extent=axs[i].get_xlim() + axs[i].get_ylim() + ) + img.set_clim(vmin=levels[0],vmax=levels[-1]) + + if i > 0: + cbar = plt.colorbar(img, ax=axs[i], orientation='vertical', + pad=0.05, shrink=0.8, **cp_info['colorbar_opt']) + #TODO keep variable attributes on dataarrays + #cbar.set_label(wrap_fields[i].attrs['units']) + #Set stats: area_avg + axs[i].set_title(f"Mean: {area_avg[i].item():5.2f}\nMax: {wrap_fields[i].max().item():5.2f}\nMin: {wrap_fields[i].min().item():5.2f}", + loc='right', fontsize=tiFontSize) + + # Custom setting for each subplot + for a in axs: + a.coastlines() + if projection=='global': + a.set_global() + a.spines['geo'].set_linewidth(1.5) #cartopy's recommended method + a.set_xticks(np.linspace(-180, 120, 6), crs=proj) + a.set_yticks(np.linspace(-90, 90, 7), crs=proj) + a.tick_params('both', length=5, width=1.5, which='major') + a.tick_params('both', length=5, width=1.5, which='minor') + a.xaxis.set_major_formatter(lon_formatter) + a.yaxis.set_major_formatter(lat_formatter) + elif projection == 'arctic': + a.set_extent([-180, 180, 50, 90], ccrs.PlateCarree()) + # __Follow the cartopy gallery example to make circular__: + # Compute a circle in axes coordinates, which we can use as a boundary + # for the map. We can pan/zoom as much as we like - the boundary will be + # permanently circular. + theta = np.linspace(0, 2*np.pi, 100) + center, radius = [0.5, 0.5], 0.5 + verts = np.vstack([np.sin(theta), np.cos(theta)]).T + circle = mpl.path.Path(verts * radius + center) + a.set_boundary(circle, transform=a.transAxes) + a.gridlines(draw_labels=False, crs=ccrs.PlateCarree(), + lw=1, color="gray",y_inline=True, + xlocs=range(-180,180,90), ylocs=range(0,90,10)) + + st = fig.suptitle(wks.stem[:-5].replace("_"," - "), fontsize=18) + st.set_y(0.85) + + #Set plot titles + case_title = "$\mathbf{Test}:$"+f"{case_nickname}\nyears: {case_climo_yrs[0]}-{case_climo_yrs[-1]}" + axs[0].set_title(case_title, loc='left', fontsize=tiFontSize) + if obs: + obs_var = kwargs["obs_var_name"] + obs_title = kwargs["obs_file"][:-3] + base_title = "$\mathbf{Baseline}:$"+obs_title+"\n"+"$\mathbf{Variable}:$"+f"{obs_var}" + axs[1].set_title(base_title, loc='left', fontsize=tiFontSize) + else: + base_title = "$\mathbf{Baseline}:$"+f"{base_nickname}\nyears: {baseline_climo_yrs[0]}-{baseline_climo_yrs[-1]}" + axs[1].set_title(base_title, loc='left', fontsize=tiFontSize) + axs[2].set_title("$\mathbf{Test} - \mathbf{Baseline}$", loc='left', fontsize=tiFontSize) + axs[2].set_title(f"RMSE: {d_rmse:.3f}", fontsize=tiFontSize) + axs[3].set_title("Test % Diff Baseline", loc='left', fontsize=tiFontSize,fontweight="bold") + + fig.savefig(wks, bbox_inches='tight', dpi=300) + + #Close plots: + plt.close() + +## End of plot_unstructured_map_and_save + # -- vertical interpolation code -- # @@ -1835,6 +2178,12 @@ def prep_contour_plot(adata, bdata, diffdata, pctdata, **kwargs): minval = np.min([np.min(adata), np.min(bdata)]) maxval = np.max([np.max(adata), np.max(bdata)]) + # determine levels & color normalization: + minval = np.min([np.min(adata), np.min(bdata)]) + maxval = np.max([np.max(adata), np.max(bdata)]) + absmaxdif = np.max(np.abs(diffdata.data)) + absmaxpct = np.max(np.abs(pctdata)) + # determine norm to use (deprecate this once minimum MPL version is high enough) normfunc, mplv = use_this_norm() @@ -1902,7 +2251,7 @@ def prep_contour_plot(adata, bdata, diffdata, pctdata, **kwargs): levelsdiff = np.arange(*kwargs['diff_contour_range']) else: # set a symmetric color bar for diff: - absmaxdif = np.max(np.abs(diffdata)) + absmaxdif = np.max(np.abs(diffdata.data)) # set levels for difference plot: levelsdiff = np.linspace(-1*absmaxdif, absmaxdif, 12) @@ -1933,6 +2282,30 @@ def prep_contour_plot(adata, bdata, diffdata, pctdata, **kwargs): else: normdiff = mpl.colors.Normalize(vmin=np.min(levelsdiff), vmax=np.max(levelsdiff)) + #NOTE: Sometimes the contour levels chosen in the defaults file + #can result in the "contourf" software stack generating a + #'TypologyException', which should manifest itself as a + #"PredicateError", but due to bugs in the stack itself + #will also sometimes raise an AttributeError. + + #To prevent this from happening, the polar max and min values + #are calculated, and if the default contour values are significantly + #larger then the min-max values, then the min-max values are used instead: + #------------------------------- + if max(levels1) > 10*maxval: + levels1 = np.linspace(minval, maxval, 12) + norm1 = mpl.colors.Normalize(vmin=minval, vmax=maxval) + elif minval < 0 and min(levels1) < 10*minval: + levels1 = np.linspace(minval, maxval, 12) + norm1 = mpl.colors.Normalize(vmin=minval, vmax=maxval) + #End if + + if max(np.abs(levelsdiff)) > 10*absmaxdif: + levelsdiff = np.linspace(-1*absmaxdif, absmaxdif, 12) + + #End if + #------------------------------- + subplots_opt = {} contourf_opt = {} colorbar_opt = {} @@ -1967,7 +2340,7 @@ def prep_contour_plot(adata, bdata, diffdata, pctdata, **kwargs): def plot_zonal_mean_and_save(wks, case_nickname, base_nickname, case_climo_yrs, baseline_climo_yrs, - adata, bdata, has_lev, log_p, obs=False, **kwargs): + adata, bdata, has_lev, log_p=False, obs=False, **kwargs): """This is the default zonal mean plot @@ -2035,8 +2408,12 @@ def plot_zonal_mean_and_save(wks, case_nickname, base_nickname, # calculate the percent change pct = (azm - bzm) / np.abs(bzm) * 100.0 #check if pct has NaN's or Inf values and if so set them to 0 to prevent plotting errors - pct = pct.where(np.isfinite(pct), np.nan) - pct = pct.fillna(0.0) + pct_0 = pct.where(np.isfinite(pct), np.nan) + pct_0 = pct_0.fillna(0.0) + if isinstance(pct, ux.UxDataArray): + pct = ux.UxDataArray(pct_0) + else: + pct = pct_0 # generate dictionary of contour plot settings: cp_info = prep_contour_plot(azm, bzm, diff, pct, **kwargs) @@ -2104,8 +2481,12 @@ def plot_zonal_mean_and_save(wks, case_nickname, base_nickname, # calculate the percent change pct = (azm - bzm) / np.abs(bzm) * 100.0 #check if pct has NaN's or Inf values and if so set them to 0 to prevent plotting errors - pct = pct.where(np.isfinite(pct), np.nan) - pct = pct.fillna(0.0) + pct_0 = pct.where(np.isfinite(pct), np.nan) + pct_0 = pct_0.fillna(0.0) + if isinstance(pct, ux.UxDataArray): + pct = ux.UxDataArray(pct_0) + else: + pct = pct_0 fig, ax = plt.subplots(nrows=3) ax = [ax[0],ax[1],ax[2]] @@ -2145,7 +2526,7 @@ def plot_zonal_mean_and_save(wks, case_nickname, base_nickname, def plot_meridional_mean_and_save(wks, case_nickname, base_nickname, case_climo_yrs, baseline_climo_yrs, - adata, bdata, has_lev, latbounds=None, obs=False, **kwargs): + adata, bdata, has_lev, log_p=False, latbounds=None, obs=False, **kwargs): """Default meridional mean plot @@ -2255,8 +2636,12 @@ def plot_meridional_mean_and_save(wks, case_nickname, base_nickname, # calculate the percent change pct = (adata - bdata) / np.abs(bdata) * 100.0 #check if pct has NaN's or Inf values and if so set them to 0 to prevent plotting errors - pct = pct.where(np.isfinite(pct), np.nan) - pct = pct.fillna(0.0) + pct_0 = pct.where(np.isfinite(pct), np.nan) + pct_0 = pct_0.fillna(0.0) + if isinstance(pct, ux.UxDataArray): + pct = ux.UxDataArray(pct_0) + else: + pct = pct_0 # plot-controlling parameters: xdim = 'lon' # the name used for the x-axis dimension @@ -2318,8 +2703,12 @@ def plot_meridional_mean_and_save(wks, case_nickname, base_nickname, st = fig.suptitle(wks.stem[:-5].replace("_"," - "), fontsize=15) st.set_y(0.85) ax[-1].set_xlabel("LONGITUDE") - if cp_info['plot_log_p']: + #if cp_info['plot_log_p']: + # [a.set_yscale("log") for a in ax] + + if log_p: [a.set_yscale("log") for a in ax] + fig.text(-0.03, 0.5, 'PRESSURE [hPa]', va='center', rotation='vertical') else: @@ -2467,8 +2856,12 @@ def square_contour_difference(fld1, fld2, **kwargs): pct = (fld1 - fld2) / np.abs(fld2) * 100.0 #check if pct has NaN's or Inf values and if so set them to 0 to prevent plotting errors - pct = pct.where(np.isfinite(pct), np.nan) - pct = pct.fillna(0.0) + pct_0 = pct.where(np.isfinite(pct), np.nan) + pct_0 = pct_0.fillna(0.0) + if isinstance(pct, ux.UxDataArray): + pct = ux.UxDataArray(pct_0) + else: + pct = pct_0 ## USE A DIVERGING COLORMAP CENTERED AT ZERO ## Special case is when min > 0 or max < 0 diff --git a/lib/regions_lnd.yaml b/lib/regions_lnd.yaml new file mode 100644 index 000000000..1ef070ac3 --- /dev/null +++ b/lib/regions_lnd.yaml @@ -0,0 +1,189 @@ +# Define regions with lists for boundaries of +# - lat (-90 to 90) +# - lon (-180 to 180) +# - region_category for website plotting + +Global: + lat_bounds: [-90, 90] + lon_bounds: [-180, 180] + region_category: None +N Hemisphere Land: + lat_bounds: [0, 90] + lon_bounds: [-180, 180] + region_category: None +S Hemisphere Land: + lat_bounds: [-90, 0] + lon_bounds: [-180, 180] + region_category: None +Polar: + lat_bounds: [60, 90] + lon_bounds: [-180, 180] + region_category: Polar +Alaskan Arctic: + lat_bounds: [66.5, 72] + lon_bounds: [-170, -140] + region_category: Polar +Canadian Arctic: + lat_bounds: [66.5, 90] + lon_bounds: [-120, -60] + region_category: Polar +Greenland: + lat_bounds: [60, 90] + lon_bounds: [-60, -20] + region_category: Polar +Russian Arctic: + lat_bounds: [66.5, 90] + lon_bounds: [70, 170] + region_category: Polar +Antarctica: + lat_bounds: [-90, -65] + lon_bounds: [-180, 180] + region_category: Polar +Alaska: + lat_bounds: [59, 66.5] + lon_bounds: [-170, -140] + region_category: Boreal +Northwest Canada: + lat_bounds: [55, 66.5] + lon_bounds: [-125, -100] + region_category: Boreal +Central Canada: + lat_bounds: [50, 62] + lon_bounds: [-100, -80] + region_category: Boreal +Eastern Canada: + lat_bounds: [50, 60] + lon_bounds: [-80, -55] + region_category: Boreal +Northern Europe: + lat_bounds: [60, 70] + lon_bounds: [5, 45] + region_category: Boreal +Western Siberia: + lat_bounds: [55, 66.5] + lon_bounds: [60, 90] + region_category: Boreal +Eastern Siberia: + lat_bounds: [50, 66.5] + lon_bounds: [90, 140] + region_category: Boreal +Lost Boreal Forest: + lat_bounds: [48, 56] + lon_bounds: [95, 103] + region_category: Boreal +Western US: + lat_bounds: [30, 50] + lon_bounds: [-130, -105] + region_category: Temperate +Central US: + lat_bounds: [30, 50] + lon_bounds: [-105, -90] + region_category: Temperate +Eastern US: + lat_bounds: [30, 50] + lon_bounds: [-90, -70] + region_category: Temperate +CONUS: + lat_bounds: [25, 55] + lon_bounds: [-125, -70] + region_category: Temperate +Europe: + lat_bounds: [45, 60] + lon_bounds: [-10, 30] + region_category: Temperate +Mediterranean: + lat_bounds: [34, 45] + lon_bounds: [-10, 30] + region_category: Temperate +Central America: + lat_bounds: [5, 16] + lon_bounds: [-95, -75] + region_category: Tropical +Amazonia: + lat_bounds: [-10, 0] + lon_bounds: [-70, -50] + region_category: Tropical +Central Africa: + lat_bounds: [-5, 5] + lon_bounds: [10, 30] + region_category: Tropical +Indonesia: + lat_bounds: [-10, 10] + lon_bounds: [90, 150] + region_category: Tropical +S America: + lat_bounds: [-30, 5] + lon_bounds: [-85, -35] + region_category: Tropical +Brazil: + lat_bounds: [-23.5, -10] + lon_bounds: [-65, -30] + region_category: Savanna +Sahel: + lat_bounds: [6, 16] + lon_bounds: [-5, 15] + region_category: Savanna +Southern Africa: + lat_bounds: [-23.5, -5] + lon_bounds: [10, 40] + region_category: Savanna +India: + lat_bounds: [10, 23.5] + lon_bounds: [70, 90] + region_category: Savanna +Indochina: + lat_bounds: [10, 23.5] + lon_bounds: [90, 120] + region_category: Savanna +Sahara Desert: + lat_bounds: [16, 30] + lon_bounds: [-20, 30] + region_category: Arid +Arabian Peninsula: + lat_bounds: [16, 30] + lon_bounds: [35, 60] + region_category: Arid +Australia: + lat_bounds: [-30, -20] + lon_bounds: [110, 145] + region_category: Arid +Central Asia: + lat_bounds: [35, 50] + lon_bounds: [55, 70] + region_category: Arid +Mongolia: + lat_bounds: [40, 50] + lon_bounds: [85, 120] + region_category: Arid +Tibetan Plateau: + lat_bounds: [30, 40] + lon_bounds: [80, 100] + region_category: Asia +Central Asia 2: + lat_bounds: [40, 50] + lon_bounds: [40, 100] + region_category: Asia +NE China: + lat_bounds: [40, 50] + lon_bounds: [100, 130] + region_category: Asia +Eastern China: + lat_bounds: [30, 40] + lon_bounds: [100, 120] + region_category: Asia +Southern Asia: + lat_bounds: [20, 30] + lon_bounds: [60, 120] + region_category: Asia +Sahara and Arabia: + lat_bounds: [15, 30] + lon_bounds: [-15, 60] + region_category: Arid +MedSea and MidEast: + lat_bounds: [30, 45] + lon_bounds: [-10, 60] + region_category: Arid +Tigris Euphrates: + lat_bounds: [30, 40] + lon_bounds: [37, 50] + region_category: Arid diff --git a/lib/utils.py b/lib/utils.py new file mode 100644 index 000000000..70987365a --- /dev/null +++ b/lib/utils.py @@ -0,0 +1,338 @@ +# utils.py + +def check_unstructured(ds, case): + """ + Check if a dataset is unstructured based on its dimensions. + """ + if ('lat' not in ds.dims) and ('lon' not in ds.dims): + if ('ncol' in ds.dims) or ('lndgrid' in ds.dims): + message = f"Looks like the case '{case}' is unstructured, eh!" + print(message) + return True + return False + + +from pathlib import Path +import os +from adf_base import AdfError + +def grid_timeseries(**kwargs): + #regrd_ts_loc = Path(test_output_loc[case_idx]) + # Check if time series directory exists, and if not, then create it: + # Use pathlib to create parent directories, if necessary. + + ts_dir = Path(kwargs["ts_dir"]) + method = kwargs["method"] + weight_file = kwargs["wgts_file"] + latlon_file = kwargs["latlon_file"] + time_file = kwargs["time_file"] + comp = kwargs["comp"] + diag_var_list = kwargs["diag_var_list"] + case_name = kwargs["case_name"] + hist_str = kwargs["hist_str"] + time_string = kwargs["time_string"] + + regrd_ts_loc = ts_dir / "regrid" + Path(regrd_ts_loc).mkdir(parents=True, exist_ok=True) + # Check that path actually exists: + if not regrd_ts_loc.is_dir(): + print(f" {regrd_ts_loc} not found, making new directory") + regrd_ts_loc.mkdir(parents=True) + + #Check if any a weights file exists if using native grid, OPTIONAL + if not latlon_file: + msg = "WARNING: This looks like an unstructured case, but missing weights file, can't continue." + raise AdfError(msg) + + for var in diag_var_list: + print("VAR",var,"\n") + ts_ds = xr.open_dataset(sorted(ts_dir.glob(f"*.{var}.*nc"))[0]) + + # Store the original cftime time values + #print("ts_ds['time']",ts_ds['time'],"\n\n") + original_time = ts_ds['time'].values + + rgdata = unstructure_regrid(ts_ds, var, comp=comp, + wgt_file=weight_file, + latlon_file=latlon_file, + time_file=time_file, + method=method) + # Copy global attributes + rgdata.attrs = ts_ds.attrs.copy() + attrs_dict = { + #"adf_user": adf.user, + #"climo_yrs": f"{case_name}: {syear}-{eyear}", + #"climatology_files": climatology_files_str, + "native_grid_to_latlon":f"xesmf Regridder; method: {method}" + } + ts_outfil_str = (str(ts_dir) + + os.sep + + ".".join([case_name, hist_str, var, time_string, "nc"]) + ) + regridded_file_loc = regrd_ts_loc / Path(ts_outfil_str).parts[-1].replace(".nc","_gridded.nc") + #rgdata = rgdata.assign_attrs(attrs_dict) + # Restore the original cftime time values + rgdata = rgdata.assign_coords(time=('time', original_time)) + #print("regridded_file_loc",rgdata.time,"\n\n") + save_to_nc(rgdata, regridded_file_loc) + #self.adf.native_grid[f"{case_type_string}_native_grid"] = False + + #file_path = os.path.join(dir_path, file_name) + #os.remove(ts_outfil_str) + #print("ts_outfil_str before death: ",ts_outfil_str,"\n") + #sorted(ts_dir.glob(f"*.{var}.*nc"))[0].unlink() + + + + +# Regrids unstructured SE grid to regular lat-lon +# Shamelessly borrowed from @maritsandstad with NorESM who deserves credit for this work +# https://github.com/NorESMhub/xesmf_clm_fates_diagnostic/blob/main/src/xesmf_clm_fates_diagnostic/plotting_methods.py + +import xarray as xr +import xesmf +import numpy as np + +def make_se_ts_regridder(weight_file,s_data,d_data, + Method='coservative' + ): + # Intialize dict for xesmf.Regridder + #regridder_kwargs = {} + + if weight_file: + weights = xr.open_dataset(weight_file) + #regridder_kwargs['weights'] = weights + else: + print("No weights file given!") + # regridder_kwargs['method'] = 'coservative' + + in_shape = weights.src_grid_dims.load().data + + # Since xESMF expects 2D vars, we'll insert a dummy dimension of size-1 + if len(in_shape) == 1: + in_shape = [1, in_shape.item()] + + # output variable shape + out_shape = weights.dst_grid_dims.load().data.tolist()[::-1] + + dummy_in = xr.Dataset( + { + "lat": ("lat", np.empty((in_shape[0],))), + "lon": ("lon", np.empty((in_shape[1],))), + } + ) + dummy_out = xr.Dataset( + { + "lat": ("lat", weights.yc_b.data.reshape(out_shape)[:, 0]), + "lon": ("lon", weights.xc_b.data.reshape(out_shape)[0, :]), + } + ) + + if isinstance(s_data, xr.DataArray): + s_mask = xr.DataArray(s_data.data.reshape(in_shape[0],in_shape[1]), dims=("lat", "lon")) + dummy_in['mask']= s_mask + if isinstance(d_data, xr.DataArray): + d_mask = xr.DataArray(d_data.values, dims=("lat", "lon")) + dummy_out['mask']= d_mask + + # do source and destination grids need masks here? + # See xesmf docs https://xesmf.readthedocs.io/en/stable/notebooks/Masking.html#Regridding-with-a-mask + regridder = xesmf.Regridder( + dummy_in, + dummy_out, + weights=weight_file, + # results seem insensitive to this method choice + # choices are coservative_normed, coservative, and bilinear + method=Method, + reuse_weights=True, + periodic=True, + ) + return regridder + +import xarray as xr +import xesmf +import numpy as np + +#def unstructure_regrid(model_dataset, var_name, comp, weight_file, latlon_file, method): +#def unstructure_regrid(model_dataset, var_name, comp, wgt_file, method, latlon_file=None): +def unstructure_regrid(model_dataset, var_name, comp, wgt_file, method, latlon_file, time_file, **kwargs): + """ + Function that takes a variable from a model xarray + dataset, regrids it to another dataset's lat/lon + coordinates (if applicable) + ---------- + model_dataset -> The xarray dataset which contains the model variable data + var_name -> The name of the variable to be regridded/interpolated. + comp -> + wgt_file -> + method -> + latlon_file -> + + Optional inputs: + + kwargs -> Keyword arguments that contain paths to THE REST IS NOT APPLICABLE: surface pressure + and mid-level pressure files, which are necessary for + certain types of vertical interpolation. + This function returns a new xarray dataset that contains the gridded + model variable. + """ + + #Import ADF-specific functions: + from regrid_se_to_fv import make_se_regridder, regrid_se_data_conservative, regrid_se_data_bilinear, regrid_atm_se_data_conservative, regrid_atm_se_data_bilinear + + if comp == "atm": + comp_grid = "ncol" + if comp == "lnd": + comp_grid = "lndgrid" + if latlon_file: + latlon_ds = xr.open_dataset(latlon_file) + else: + print("Looks like no lat lon file is supplied. God speed!") + + model_dataset[var_name] = model_dataset[var_name].fillna(0) + #mdata = model_dataset[var_name] + + if comp == "lnd": + model_dataset['landfrac'] = model_dataset['landfrac'].fillna(0) + #mdata = mdata * model_dataset.landfrac # weight flux by land frac + model_dataset[var_name] = model_dataset[var_name] * model_dataset.landfrac # weight flux by land frac + s_data = model_dataset.landmask#.isel(time=0) + d_data = latlon_ds.landmask + + """# Combine dimensions from both datasets while keeping ds2 attributes + d_data = xr.Dataset( + coords={"lat": latlon_ds["lat"], "lon": latlon_ds["lon"], "time": time_file["time"]}, + attrs=latlon_ds.attrs # Copy attributes from ds2 + ) + print("AHHHHHH",d_data,"\n\n") + # Add the 'temperature' variable from ds2 to new_ds + d_data["landmask"] = time_file["landmask"] + print("AHHHHHH2",d_data,"\n\n") + d_data = d_data.landmask""" + else: + s_data = None #mdata.isel(time=0) + d_data = None #latlon_ds[var_name] + print("AHHHHHH3",d_data,"\n\n") + #Grid model data to match target grid lat/lon: + regridder = make_se_ts_regridder(weight_file=wgt_file, + s_data = s_data, + d_data = d_data, + Method = method, + ) + + if comp == "lnd": + if method == 'coservative': + rgdata = regrid_se_data_conservative(regridder, model_dataset, comp_grid) + if method == 'bilinear': + rgdata = regrid_se_data_bilinear(regridder, model_dataset, comp_grid) + rgdata[var_name] = (rgdata[var_name] / rgdata.landfrac) + + if comp == "atm": + if method == 'coservative': + rgdata = regrid_atm_se_data_conservative(regridder, model_dataset, comp_grid) + if method == 'bilinear': + rgdata = regrid_atm_se_data_bilinear(regridder, model_dataset, comp_grid) + + + #rgdata['lat'] = latlon_ds.lat #??? + if comp == "lnd": + rgdata['landmask'] = latlon_ds.landmask + rgdata['landfrac'] = rgdata.landfrac#.isel(time=0) + + """new_ds = xr.Dataset( + coords={"lat": ds1["lat"], "lon": ds1["lon"], "time": ds2["time"]}, + attrs=ds2.attrs # Copy attributes from ds2 + ) + """ + # calculate area + rgdata = _calc_area(rgdata) + + #Return dataset: + return rgdata + + +def regrid_atm_se_data_bilinear(regridder, data_to_regrid, comp_grid='ncol'): + if isinstance(data_to_regrid, xr.Dataset): + vars_with_ncol = [name for name in data_to_regrid.variables if comp_grid in data_to_regrid[name].dims] + updated = data_to_regrid.copy().update(data_to_regrid[vars_with_ncol].transpose(..., comp_grid).expand_dims("dummy", axis=-2)) + elif isinstance(data_to_regrid, xr.DataArray): + updated = data_to_regrid.transpose(...,comp_grid).expand_dims("dummy",axis=-2) + else: + raise ValueError(f"Something is wrong because the data to regrid isn't xarray: {type(data_to_regrid)}") + regridded = regridder(updated) + return regridded + + +def regrid_atm_se_data_conservative(regridder, data_to_regrid, comp_grid='ncol'): + if isinstance(data_to_regrid, xr.Dataset): + vars_with_ncol = [name for name in data_to_regrid.variables if comp_grid in data_to_regrid[name].dims] + updated = data_to_regrid.copy().update(data_to_regrid[vars_with_ncol].transpose(..., comp_grid).expand_dims("dummy", axis=-2)) + elif isinstance(data_to_regrid, xr.DataArray): + updated = data_to_regrid.transpose(...,comp_grid).expand_dims("dummy",axis=-2) + else: + raise ValueError(f"Something is wrong because the data to regrid isn't xarray: {type(data_to_regrid)}") + regridded = regridder(updated,skipna=True, na_thres=1) + return regridded + + +def regrid_lnd_se_data_bilinear(regridder, data_to_regrid, comp_grid): + updated = data_to_regrid.copy().transpose(..., comp_grid).expand_dims("dummy", axis=-2) + regridded = regridder(updated.rename({"dummy": "lat", comp_grid: "lon"}), + skipna=True, na_thres=1, + ) + return regridded + + +def regrid_lnd_se_data_conservative(regridder, data_to_regrid, comp_grid): + updated = data_to_regrid.copy().transpose(..., comp_grid).expand_dims("dummy", axis=-2) + regridded = regridder(updated.rename({"dummy": "lat", comp_grid: "lon"}) ) + return regridded + + + +def save_to_nc(tosave, outname, attrs=None, proc=None): + """Saves xarray variable to new netCDF file""" + + xo = tosave # used to have more stuff here. + # deal with getting non-nan fill values. + if isinstance(xo, xr.Dataset): + enc_dv = {xname: {'_FillValue': None} for xname in xo.data_vars} + else: + enc_dv = {} + #End if + enc_c = {xname: {'_FillValue': None} for xname in xo.coords} + enc = {**enc_c, **enc_dv} + if attrs is not None: + xo.attrs = attrs + if proc is not None: + xo.attrs['Processing_info'] = f"Start from file {origname}. " + proc + xo.to_netcdf(outname, format='NETCDF4', encoding=enc) + + + +def _calc_area(rgdata): + # calculate area + area_km2 = np.zeros(shape=(len(rgdata['lat']), len(rgdata['lon']))) + earth_radius_km = 6.37122e3 # in meters + + yres_degN = np.abs(np.diff(rgdata['lat'].data)) # distances between gridcell centers... + xres_degE = np.abs(np.diff(rgdata['lon'])) # ...end up with one less element, so... + yres_degN = np.append(yres_degN, yres_degN[-1]) # shift left (edges <-- centers); assume... + xres_degE = np.append(xres_degE, xres_degE[-1]) # ...last 2 distances bet. edges are equal + + dy_km = yres_degN * earth_radius_km * np.pi / 180 # distance in m + phi_rad = rgdata['lat'].data * np.pi / 180 # degrees to radians + + # grid cell area + for j in range(len(rgdata['lat'])): + for i in range(len(rgdata['lon'])): + dx_km = xres_degE[i] * np.cos(phi_rad[j]) * earth_radius_km * np.pi / 180 # distance in m + area_km2[j,i] = dy_km[j] * dx_km + + rgdata['area'] = xr.DataArray(area_km2, + coords={'lat': rgdata.lat, 'lon': rgdata.lon}, + dims=["lat", "lon"]) + rgdata['area'].attrs['units'] = 'km2' + rgdata['area'].attrs['long_name'] = 'Grid cell area' + + return rgdata diff --git a/lib/website_templates/template.html b/lib/website_templates/template.html index 3a06250cb..85dba9860 100644 --- a/lib/website_templates/template.html +++ b/lib/website_templates/template.html @@ -1,7 +1,7 @@ - ADF {{var_title}} + CLM Diagnostics {{var_title}} @@ -22,8 +22,8 @@
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    • diff --git a/lib/website_templates/template_index.html b/lib/website_templates/template_index.html index db49975e1..9e411c410 100644 --- a/lib/website_templates/template_index.html +++ b/lib/website_templates/template_index.html @@ -1,7 +1,7 @@ - ADF Diagnostics + CLM Diagnostics @@ -15,8 +15,8 @@
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    • diff --git a/lib/website_templates/template_mean_diag.html b/lib/website_templates/template_mean_diag.html index a62250af8..e748d93a7 100644 --- a/lib/website_templates/template_mean_diag.html +++ b/lib/website_templates/template_mean_diag.html @@ -1,7 +1,7 @@ - ADF Mean Plots + LDF Mean Plots @@ -22,8 +22,8 @@
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    • diff --git a/lib/website_templates/template_mean_tables.html b/lib/website_templates/template_mean_tables.html index 877d96e26..d6c53dee8 100644 --- a/lib/website_templates/template_mean_tables.html +++ b/lib/website_templates/template_mean_tables.html @@ -1,7 +1,7 @@ - ADF Mean Tables + LDF Mean Tables @@ -22,8 +22,8 @@
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    • @@ -38,7 +38,7 @@

    Baseline Case:

    -

    AMWG Tables

    +

    LMWG Tables

    diff --git a/lib/website_templates/template_multi_case_index.html b/lib/website_templates/template_multi_case_index.html index 0ba930dec..4ff607e98 100644 --- a/lib/website_templates/template_multi_case_index.html +++ b/lib/website_templates/template_multi_case_index.html @@ -1,7 +1,7 @@ - ADF diagnostics + CLM Diagnostics @@ -12,8 +12,8 @@
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    • diff --git a/lib/website_templates/template_table.html b/lib/website_templates/template_table.html index 82158dd2c..581af913b 100644 --- a/lib/website_templates/template_table.html +++ b/lib/website_templates/template_table.html @@ -1,7 +1,7 @@ - ADF Mean Tables + LDF Mean Tables @@ -22,8 +22,8 @@
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    • @@ -38,7 +38,7 @@

    Baseline Case:

    -

    AMWG Tables

    +

    LMWG Tables

    diff --git a/scripts/analysis/lmwg_table.py b/scripts/analysis/lmwg_table.py new file mode 100644 index 000000000..9d7d814b1 --- /dev/null +++ b/scripts/analysis/lmwg_table.py @@ -0,0 +1,411 @@ +import numpy as np +import xarray as xr +import sys +from pathlib import Path +import warnings # use to warn user about missing files. + +#Import "special" modules: +try: + import scipy.stats as stats # for easy linear regression and testing +except ImportError: + print("Scipy module does not exist in python path, but is needed for lmwg_table.") + print("Please install module, e.g. 'pip install scipy'.") + sys.exit(1) +#End except + +try: + import pandas as pd +except ImportError: + print("Pandas module does not exist in python path, but is needed for lmwg_table.") + print("Please install module, e.g. 'pip install pandas'.") + sys.exit(1) +#End except + +#Import ADF-specific modules: +import plotting_functions as pf + +def lmwg_table(adf): + + """ + Main function goes through series of steps: + - load the variable data + - Determine whether there are spatial dims; if yes, do global average (TODO: regional option) + - Apply annual average (TODO: add seasonal here) + - calculates the statistics + + mean + + sample size + + standard deviation + + standard error of the mean + + 5/95% confidence interval of the mean + + linear trend + + p-value of linear trend + - puts statistics into a CSV file + - generates simple HTML that can display the data + + Description of needed inputs from ADF: + + case_names -> Name(s) of CAM case provided by "cam_case_name" + input_ts_locs -> Location(s) of CAM time series files provided by "cam_ts_loc" + output_loc -> Location to write AMWG table files to, provided by "cam_diag_plot_loc" + var_list -> List of CAM output variables provided by "diag_var_list" + var_defaults -> Dict that has keys that are variable names and values that are plotting preferences/defaults. + + and if doing a CAM baseline comparison: + + baseline_name -> Name of CAM baseline case provided by "cam_case_name" + input_ts_baseline -> Location of CAM baseline time series files provied by "cam_ts_loc" + + """ + + #Import necessary modules: + from adf_base import AdfError + + #Additional information: + #---------------------- + + # GOAL: replace the "Tables" set in AMWG + # Set Description + # 1 Tables of ANN, DJF, JJA, global and regional means and RMSE. + # + # STRATEGY: + # I think the right solution is to generate one CSV (or other?) file that + # contains all of the data. + # So we need: + # - a function that would produces the data, and + # - then call a function that adds the data to a file + # - another function(module?) that uses the file to produce a "web page" + + # IMPLEMENTATION: + # - assume that we will have time series of global averages already ... that should be done ahead of time + # - given a variable or file for a variable (equivalent), we will calculate the all-time, DJF, JJA, MAM, SON + # + mean + # + standard error of the mean + # -- 95% confidence interval for the mean, estimated by: + # ---- CI95 = mean + (SE * 1.96) + # ---- CI05 = mean - (SE * 1.96) + # + standard deviation + # AMWG also includes the RMSE b/c it is comparing two things, but I will put that off for now. + + # DETAIL: we use python's type hinting as much as possible + + # in future, provide option to do multiple domains + # They use 4 pre-defined domains: + # NOTE, this is likely not as critical for LMWG_table, and won't work we'll with unstructured data + domains = {"global": (0, 360, -90, 90), + "tropics": (0, 360, -20, 20), + "southern": (0, 360, -90, -20), + "northern": (0, 360, 20, 90)} + + # and then in time it is DJF JJA ANN + + # within each domain and season + # the result is just a table of + # VARIABLE-NAME, RUN VALUE, OBS VALUE, RUN-OBS, RMSE + #---------------------- + + #Extract needed quantities from ADF object: + #----------------------------------------- + var_list = adf.diag_var_list + var_defaults = adf.variable_defaults + + #Check if ocean or land fraction exist + #in the variable list: + for var in ["OCNFRAC", "LANDFRAC"]: + if var in var_list: + #If so, then move them to the front of variable list so + #that they can be used to mask or vertically interpolate + #other model variables if need be: + var_idx = var_list.index(var) + var_list.pop(var_idx) + var_list.insert(0,var) + #End if + #End if + + #Special ADF variable which contains the output paths for + #all generated plots and tables for each case: + output_locs = adf.plot_location + + #CAM simulation variables (these quantities are always lists): + case_names = adf.get_cam_info("cam_case_name", required=True) + input_ts_locs = adf.get_cam_info("cam_ts_loc", required=True) + + #Check if a baseline simulation is also being used: + if not adf.get_basic_info("compare_obs"): + #Extract CAM baseline variaables: + baseline_name = adf.get_baseline_info("cam_case_name", required=True) + input_ts_baseline = adf.get_baseline_info("cam_ts_loc", required=True) + + case_names.append(baseline_name) + input_ts_locs.append(input_ts_baseline) + + #Save the baseline to the first case's plots directory: + output_locs.append(output_locs[0]) + else: + print("AMWG table doesn't currently work with obs, so obs table won't be created.") + #End if + + #----------------------------------------- + + #Loop over CAM cases: + #Initialize list of case name csv files for case comparison check later + csv_list = [] + for case_idx, case_name in enumerate(case_names): + + #Convert output location string to a Path object: + output_location = Path(output_locs[case_idx]) + + #Generate input file path: + input_location = Path(input_ts_locs[case_idx]) + + #Check that time series input directory actually exists: + if not input_location.is_dir(): + errmsg = f"Time series directory '{input_location}' not found. Script is exiting." + raise AdfError(errmsg) + #Write to debug log if enabled: + adf.debug_log(f"DEBUG: location of files is {str(input_location)}") + + #Notify user that script has started: + print(f"\n Calculating AMWG variable table for '{case_name}'...") + + #Create output file name: + output_csv_file = output_location / f"amwg_table_{case_name}.csv" + + #Given that this is a final, user-facing analysis, go ahead and re-do it every time: + if Path(output_csv_file).is_file(): + Path.unlink(output_csv_file) + #End if + + #Create/reset new variable that potentially stores the re-gridded + #ocean fraction xarray data-array: + ocn_frc_da = None + + #Loop over CAM output variables: + for var in var_list: + + #Notify users of variable being added to table: + print(f"\t - Variable '{var}' being added to table") + + #Create list of time series files present for variable: + ts_filenames = f'{case_name}.*.{var}.*nc' + ts_files = sorted(input_location.glob(ts_filenames)) + + # If no files exist, try to move to next variable. --> Means we can not proceed with this variable, and it'll be problematic later. + if not ts_files: + errmsg = f"Time series files for variable '{var}' not found. Script will continue to next variable." + warnings.warn(errmsg) + continue + #End if + + #TEMPORARY: For now, make sure only one file exists: + if len(ts_files) != 1: + errmsg = "Currently the AMWG table script can only handle one time series file per variable." + errmsg += f" Multiple files were found for the variable '{var}', so it will be skipped." + print(errmsg) + continue + #End if + + #Load model variable data from file: + ds = pf.load_dataset(ts_files) + weights = ds.landfrac * ds.area + data = ds[var] + + #Extract defaults for variable: + var_default_dict = var_defaults.get(var, {}) + scale_factor = var_default_dict.get('scale_factor', 1) + scale_factor_table = var_default_dict.get('scale_factor_table', 1) + add_offset = var_default_dict.get('add_offset', 0) + # could require this for each variable? + avg_method = var_default_dict.get('avg_method', 'mean') + if avg_method == 'mean': + weights = weights/weights.sum() + + # get units for variable (do this before doing math) + data.attrs['units'] = var_default_dict.get("new_unit", data.attrs.get('units', 'none')) + data.attrs['units'] = var_default_dict.get("table_unit", data.attrs.get('units', 'none')) + if hasattr(data, 'units'): + unit_str = data.attrs['units'] + else: + unit_str = '--' + + data = data * scale_factor * scale_factor_table + #Check if variable has a vertical coordinate: + if 'lev' in data.coords or 'ilev' in data.coords: + print(f"\t ** Variable '{var}' has a vertical dimension, "+\ + "which is currently not supported for the AMWG Table. Skipping...") + #Skip this variable and move to the next variable in var_list: + continue + #End if + + #Check if variable should be masked: + if 'mask' in var_default_dict: + if var_default_dict['mask'].lower() == 'ocean': + #Check if the ocean fraction has already been regridded + #and saved: + if ocn_frc_da is not None: + ofrac = ocn_frc_da + # set the bounds of regridded ocnfrac to 0 to 1 + ofrac = xr.where(ofrac>1,1,ofrac) + ofrac = xr.where(ofrac<0,0,ofrac) + + # apply ocean fraction mask to variable + data = pf.mask_land_or_ocean(data, ofrac, use_nan=True) + #data = var_tmp + else: + print(f"OCNFRAC not found, unable to apply mask to '{var}'") + #End if + else: + #Currently only an ocean mask is supported, so print warning here: + wmsg = "Currently the only variable mask option is 'ocean'," + wmsg += f"not '{var_default_dict['mask'].lower()}'" + print(wmsg) + #End if + #End if + + #If the variable is ocean fraction, then save the dataset for use later: + if var == 'OCNFRAC': + ocn_frc_da = data + #End if + + # we should check if we need to do area averaging: + if len(data.dims) > 1: + # flags that we have spatial dimensions + # Note: that could be 'lev' which should trigger different behavior + # Note: we should be able to handle (lat, lon) or (ncol,) cases, at least + # data = pf.spatial_average(data) # changes data "in place" + data = pf.spatial_average_lnd(data,weights) # hard code for land + # TODO, make this optional for lmwg_tables of amwg_table + # In order to get correct statistics, average to annual or seasonal + data = pf.annual_mean(data, whole_years=True, time_name='time') + + # create a dataframe: + cols = ['variable', 'unit', 'mean', 'sample size', 'standard dev.', + 'standard error', '95% CI', 'trend', 'trend p-value'] + + # These get written to our output file: + stats_list = _get_row_vals(data) + row_values = [var, unit_str] + stats_list + + # Format entries: + dfentries = {c:[row_values[i]] for i,c in enumerate(cols)} + + # Add entries to Pandas structure: + df = pd.DataFrame(dfentries) + + # Check if the output CSV file exists, + # if so, then append to it: + if output_csv_file.is_file(): + df.to_csv(output_csv_file, mode='a', header=False, index=False) + else: + df.to_csv(output_csv_file, header=cols, index=False) + + #End of var_list loop + #-------------------- + + # Move RESTOM to top of table (if applicable) + #-------------------------------------------- + try: + table_df = pd.read_csv(output_csv_file) + if 'RESTOM' in table_df['variable'].values: + table_df = pd.concat([table_df[table_df['variable'] == 'RESTOM'], table_df]).reset_index(drop = True) + table_df = table_df.drop_duplicates() + table_df.to_csv(output_csv_file, header=cols, index=False) + + # last step is to add table dataframe to website (if enabled): + adf.add_website_data(table_df, case_name, case_name, plot_type="Tables") + except FileNotFoundError: + print(f"\n\tAMWG table for '{case_name}' not created.\n") + #End try/except + + #Keep track of case csv files for comparison table check later + csv_list.extend(sorted(output_location.glob(f"amwg_table_{case_name}.csv"))) + + #End of model case loop + #---------------------- + + #Start case comparison tables + #---------------------------- + #Check if observations are being compared to, if so skip table comparison... + if not adf.get_basic_info("compare_obs"): + #Check if all tables were created to compare against, if not, skip table comparison... + if len(csv_list) != len(case_names): + print("\tNot enough cases to compare, skipping comparison table...") + else: + #Create comparison table for both cases + print("\n Making comparison table...") + _df_comp_table(adf, output_location, case_names) + print(" ... Comparison table has been generated successfully") + #End if + else: + print(" No comparison table will be generated due to running against obs.") + #End if + + #Notify user that script has ended: + print(" ...AMWG variable table(s) have been generated successfully.") + + +################## +# Helper functions +################## + +def _get_row_vals(data): + # Now that data is (time,), we can do our simple stats: + + data_mean = data.data.mean() + #Conditional Formatting depending on type of float + if np.abs(data_mean) < 1: + formatter = ".3g" + else: + formatter = ".3f" + + data_sample = len(data) + data_std = data.std() + data_sem = data_std / data_sample + data_ci = data_sem * 1.96 # https://en.wikipedia.org/wiki/Standard_error + data_trend = stats.linregress(data.year, data.values) + + stdev = f'{data_std.data.item() : {formatter}}' + sem = f'{data_sem.data.item() : {formatter}}' + ci = f'{data_ci.data.item() : {formatter}}' + slope_int = f'{data_trend.intercept : {formatter}} + {data_trend.slope : {formatter}} t' + pval = f'{data_trend.pvalue : {formatter}}' + + return [f'{data_mean:{formatter}}', data_sample, stdev, sem, ci, slope_int, pval] + +##### + +def _df_comp_table(adf, output_location, case_names): + import pandas as pd + # TODO, make this output an option for LMWG or AMWG table + output_csv_file_comp = output_location / "amwg_table_comp.csv" + + # * * * * * * * * * * * * * * * * * * * * * * * * * * * * + #This will be for single-case for now (case_names[0]), + #will need to change to loop as multi-case is introduced + case = output_location/f"amwg_table_{case_names[0]}.csv" + baseline = output_location/f"amwg_table_{case_names[-1]}.csv" + + #Read in test case and baseline dataframes: + df_case = pd.read_csv(case) + df_base = pd.read_csv(baseline) + + #Create a merged dataframe that contains only the variables + #contained within both the test case and the baseline: + df_merge = pd.merge(df_case, df_base, how='inner', on=['variable']) + + #Create the "comparison" dataframe: + df_comp = pd.DataFrame(dtype=object) + df_comp[['variable','unit','case']] = df_merge[['variable','unit_x','mean_x']] + df_comp['baseline'] = df_merge[['mean_y']] + + diffs = df_comp['case'].values-df_comp['baseline'].values + df_comp['diff'] = [f'{i:.3g}' if np.abs(i) < 1 else f'{i:.3f}' for i in diffs] + + #Write the comparison dataframe to a new CSV file: + cols_comp = ['variable', 'unit', 'test', 'control', 'diff'] + df_comp.to_csv(output_csv_file_comp, header=cols_comp, index=False) + + #Add comparison table dataframe to website (if enabled): + adf.add_website_data(df_comp, "Case Comparison", case_names[0], plot_type="Tables") + +############## +#END OF SCRIPT diff --git a/scripts/averaging/create_climo_files.py b/scripts/averaging/create_climo_files.py index f9f05454c..ac88e08c2 100644 --- a/scripts/averaging/create_climo_files.py +++ b/scripts/averaging/create_climo_files.py @@ -57,6 +57,7 @@ def create_climo_files(adf, clobber=False, search=None): #Import necessary modules: from pathlib import Path from adf_base import AdfError + import utils as adf_utils #Notify user that script has started: msg = "\n Calculating CAM climatologies..." @@ -75,10 +76,24 @@ def create_climo_files(adf, clobber=False, search=None): output_locs = adf.get_cam_info("cam_climo_loc", required=True) calc_climos = adf.get_cam_info("calc_cam_climo") overwrite = adf.get_cam_info("cam_overwrite_climo") + # Get start and end year for climatology computation + climo_start_year = adf.get_cam_info("climo_start_year") + climo_end_year = adf.get_cam_info("climo_end_year") #Extract simulation years: start_year = adf.climo_yrs["syears"] end_year = adf.climo_yrs["eyears"] + if climo_start_year: + if climo_start_year > end_year: + raise ValueError('Sorry, climo_start_year must be earlier than ts end year.') + start_year = max(climo_start_year, start_year) + if climo_end_year: + if climo_end_year < start_year: + raise ValueError('Sorry, climo_end_year must be later than ts start year.') + end_year = min(climo_end_year, end_year) + + comp = adf.model_component + print("\ncomp",comp,"\n") #If variables weren't provided in config file, then make them a list #containing only None-type entries: @@ -96,10 +111,20 @@ def create_climo_files(adf, clobber=False, search=None): output_bl_loc = adf.get_baseline_info("cam_climo_loc", required=True) calc_bl_climos = adf.get_baseline_info("calc_cam_climo") ovr_bl = adf.get_baseline_info("cam_overwrite_climo") + climo_baseline_start_year = adf.get_baseline_info("climo_start_year") + climo_baseline_end_year = adf.get_baseline_info("climo_end_year") #Extract baseline years: bl_syr = adf.climo_yrs["syear_baseline"] bl_eyr = adf.climo_yrs["eyear_baseline"] + if climo_baseline_start_year: + if climo_baseline_start_year > bl_eyr: + raise ValueError('Sorry, climo_end_year must be later than ts start year.') + bl_syr = max(climo_baseline_start_year, bl_syr) + if climo_baseline_end_year: + if climo_baseline_end_year < bl_syr: + raise ValueError('Sorry, climo_end_year must be later than ts start year.') + bl_eyr = min(climo_baseline_end_year, bl_eyr) #Append to case lists: case_names.append(baseline_name) @@ -136,6 +161,8 @@ def create_climo_files(adf, clobber=False, search=None): input_location = Path(input_ts_locs[case_idx]) output_location = Path(output_locs[case_idx]) + regrid_output_loc = output_location / "regrid" + #Whether to overwrite existing climo files clobber = overwrite[case_idx] @@ -192,8 +219,8 @@ def create_climo_files(adf, clobber=False, search=None): adf.debug_log(logmsg) # end_diag_script(errmsg) # Previously we would kill the run here. continue - - list_of_arguments.append((adf, ts_files, syr, eyr, output_file)) + #print("\n\nts_files",ts_files,"\n\n") + list_of_arguments.append((adf, ts_files, syr, eyr, output_file, comp)) #End of var_list loop @@ -213,7 +240,7 @@ def create_climo_files(adf, clobber=False, search=None): # # Local functions # -def process_variable(adf, ts_files, syr, eyr, output_file): +def process_variable(adf, ts_files, syr, eyr, output_file, comp): ''' Compute and save the climatology file. ''' @@ -230,6 +257,21 @@ def process_variable(adf, ts_files, syr, eyr, output_file): cam_ts_data['time'] = time cam_ts_data.assign_coords(time=time) cam_ts_data = xr.decode_cf(cam_ts_data) + elif 'time_bounds' in cam_ts_data: + time = cam_ts_data['time'] + if comp == "lnd": + if ('hist_interval' in cam_ts_data['time_bounds'].dims): + dim = 'hist_interval' + else: + dim = 'nbnd' + if comp == "atm": + dim = 'nbnd' + # NOTE: force `load` here b/c if dask & time is cftime, throws a NotImplementedError: + time = xr.DataArray(cam_ts_data['time_bounds'].load().mean(dim=dim).values, + dims=time.dims, attrs=time.attrs) + cam_ts_data['time'] = time + cam_ts_data.assign_coords(time=time) + cam_ts_data = xr.decode_cf(cam_ts_data) #Extract data subset using provided year bounds: tslice = get_time_slice_by_year(cam_ts_data.time, int(syr), int(eyr)) cam_ts_data = cam_ts_data.isel(time=tslice) diff --git a/scripts/plotting/global_latlon_map.py b/scripts/plotting/global_latlon_map.py index ab37eb274..60b5b4479 100644 --- a/scripts/plotting/global_latlon_map.py +++ b/scripts/plotting/global_latlon_map.py @@ -104,6 +104,18 @@ def global_latlon_map(adfobj): #all generated plots and tables for each case: plot_locations = adfobj.plot_location + kwargs = {} + + # + unstruct_plotting = adfobj.unstructured_plotting + print("unstruct_plotting", unstruct_plotting) + if unstruct_plotting: + kwargs["unstructured_plotting"] = unstruct_plotting + #mesh_file = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'#adfobj.mesh_file + #kwargs["mesh_file"] = mesh_file + else: + unstructured=False + print("kwargs", kwargs) #Grab case years syear_cases = adfobj.climo_yrs["syears"] eyear_cases = adfobj.climo_yrs["eyears"] @@ -125,6 +137,9 @@ def global_latlon_map(adfobj): # check if existing plots need to be redone redo_plot = adfobj.get_basic_info('redo_plot') print(f"\t NOTE: redo_plot is set to {redo_plot}") + + comp = adfobj.model_component + unstructured = False #----------------------------------------- #Determine if user wants to plot 3-D variables on @@ -177,17 +192,34 @@ def global_latlon_map(adfobj): else: base_name = adfobj.data.ref_labels[var] - # Gather reference variable data - odata = adfobj.data.load_reference_regrid_da(base_name, var) + if unstruct_plotting: + mesh_file = adfobj.mesh_files["baseline_mesh_file"] + kwargs["mesh_file"] = mesh_file + odata = adfobj.data.load_reference_climo_da(var, **kwargs) + + unstruct_base = True + odataset = adfobj.data.load_reference_climo_dataset(var, **kwargs) + area = odataset.area.isel(time=0) + landfrac = odataset.landfrac.isel(time=0) + # calculate weights + wgt_base = area * landfrac / (area * landfrac).sum() + else: + odata = adfobj.data.load_reference_regrid_da(var) + if odata is None: + + dmsg = f"\t WARNING: No regridded baseline file for {base_name} for variable `{var}`, global lat/lon mean plotting skipped." + adfobj.debug_log(dmsg) + continue + o_has_dims = pf.validate_dims(odata, ["lat", "lon", "lev"]) # T iff dims are (lat,lon) -- can't plot unless we have both + if (not o_has_dims['has_lat']) or (not o_has_dims['has_lon']): + print(f"\t WARNING: skipping global map for {var} as REFERENCE does not have both lat and lon") + continue if odata is None: - dmsg = f"\t WARNING: No regridded test file for {base_name} for variable `{var}`, global lat/lon mean plotting skipped." + dmsg = f"\t WARNING: No baseline file for {base_name} for variable `{var}`, global lat/lon mean plotting skipped." + #dmsg = f"\t WARNING: No regridded baseline file for {base_name} for variable `{var}`, will" adfobj.debug_log(dmsg) - continue - - o_has_dims = pf.validate_dims(odata, ["lat", "lon", "lev"]) # T iff dims are (lat,lon) -- can't plot unless we have both - if (not o_has_dims['has_lat']) or (not o_has_dims['has_lon']): - print(f"\t WARNING: skipping global map for {var} as REFERENCE does not have both lat and lon") + print(dmsg) continue #Loop over model cases: @@ -204,25 +236,64 @@ def global_latlon_map(adfobj): print(f" {plot_loc} not found, making new directory") plot_loc.mkdir(parents=True) - #Load re-gridded model files: - mdata = adfobj.data.load_regrid_da(case_name, var) - + if unstruct_plotting: + mesh_file = adfobj.mesh_files["test_mesh_file"][case_idx] + kwargs["mesh_file"] = mesh_file + mdata = adfobj.data.load_climo_da(case_name, var, **kwargs) + + unstruct_case = True + mdataset = adfobj.data.load_climo_dataset(case_name, var, **kwargs) + area = mdataset.area.isel(time=0) + landfrac = mdataset.landfrac.isel(time=0) + # calculate weights + wgt = area * landfrac / (area * landfrac).sum() + else: + mdata = adfobj.data.load_regrid_da(case_name, var) + #Skip this variable/case if the regridded climo file doesn't exist: + if mdata is None: + dmsg = f"\t WARNING: No regridded test file for {case_name} for variable `{var}`, global lat/lon mean plotting skipped." + adfobj.debug_log(dmsg) + continue + #Determine dimensions of variable: + has_dims = pf.validate_dims(mdata, ["lat", "lon", "lev"]) + if (not has_dims['has_lat']) or (not has_dims['has_lon']): + print(f"\t WARNING: skipping global map for {var} for case {case_name} as it does not have both lat and lon") + continue + else: # i.e., has lat&lon + if (has_dims['has_lev']) and (not pres_levs): + print(f"\t WARNING: skipping global map for {var} as it has more than lev dimension, but no pressure levels were provided") + continue #Skip this variable/case if the regridded climo file doesn't exist: if mdata is None: - dmsg = f"\t WARNING: No regridded test file for {case_name} for variable `{var}`, global lat/lon mean plotting skipped." + dmsg = f"\t WARNING: No test file for {case_name} for variable `{var}`, global lat/lon mean plotting skipped." adfobj.debug_log(dmsg) continue - - #Determine dimensions of variable: has_dims = pf.validate_dims(mdata, ["lat", "lon", "lev"]) - if (not has_dims['has_lat']) or (not has_dims['has_lon']): - print(f"\t WARNING: skipping global map for {var} for case {case_name} as it does not have both lat and lon") + if (has_dims['has_lev']) and (not pres_levs): + print(f"\t WARNING: skipping global map for {var} as it has more than lev dimension, but no pressure levels were provided") continue - else: # i.e., has lat&lon - if (has_dims['has_lev']) and (not pres_levs): - print(f"\t WARNING: skipping global map for {var} as it has more than lev dimension, but no pressure levels were provided") - continue + #Determine dimensions of variable: + if unstruct_plotting: + has_dims = {} + if len(wgt.n_face) == len(wgt_base.n_face): + vres["wgt"] = wgt + has_dims = {} + has_dims['has_lev'] = False + else: + print("The weights are different between test and baseline. Won't continue, eh.") + return + + if (not unstruct_case) and (unstruct_base): + print("Base is unstructured but Test is lat/lon. Can't continue?") + return + if (unstruct_case) and (not unstruct_base): + print("Base is lat/lon but Test is unstructured. Can't continue?") + return + if (unstruct_case) and (unstruct_base): + unstructured=True + if (not unstruct_case) and (not unstruct_base): + unstructured=False # Check output file. If file does not exist, proceed. # If file exists: # if redo_plot is true: delete it now and make plot @@ -267,19 +338,33 @@ def global_latlon_map(adfobj): # difference: each entry should be (lat, lon) dseasons[s] = mseasons[s] - oseasons[s] - + # percent change pseasons[s] = (mseasons[s] - oseasons[s]) / np.abs(oseasons[s]) * 100.0 #relative change - pf.plot_map_and_save(plot_name, case_nickname, adfobj.data.ref_nickname, - [syear_cases[case_idx],eyear_cases[case_idx]], - [syear_baseline,eyear_baseline], - mseasons[s], oseasons[s], dseasons[s], pseasons[s], - obs=adfobj.compare_obs, **vres) + # If redo_plot set to True: remove old plot, if it already exists: + if (not redo_plot) and plot_name.is_file(): + #Add already-existing plot to website (if enabled): + adfobj.debug_log(f"'{plot_name}' exists and clobber is false.") + adfobj.add_website_data(plot_name, var, case_name, category=web_category, + season=s, plot_type="LatLon") - #Add plot to website (if enabled): - adfobj.add_website_data(plot_name, var, case_name, category=web_category, - season=s, plot_type="LatLon") + #Continue to next iteration: + continue + else: + if plot_name.is_file(): + plot_name.unlink() + + pf.plot_map_and_save(plot_name, case_nickname, adfobj.data.ref_nickname, + [syear_cases[case_idx],eyear_cases[case_idx]], + [syear_baseline,eyear_baseline], + mseasons[s], oseasons[s], dseasons[s], pseasons[s], + obs=adfobj.compare_obs, unstructured=unstructured, **vres) + + #Add plot to website (if enabled): + adfobj.add_website_data(plot_name, var, case_name, category=web_category, + season=s, plot_type="LatLon") + # end if redo_plot else: # => pres_levs has values, & we already checked that lev is in mdata (has_lev) @@ -319,7 +404,7 @@ def global_latlon_map(adfobj): [syear_baseline,eyear_baseline], mseasons[s].sel(lev=pres), oseasons[s].sel(lev=pres), dseasons[s].sel(lev=pres), pseasons[s].sel(lev=pres), - obs=adfobj.compare_obs, **vres) + obs=adfobj.compare_obs, unstructured=unstructured, **vres) #Add plot to website (if enabled): adfobj.add_website_data(plot_name, f"{var}_{pres}hpa", case_name, category=web_category, @@ -546,7 +631,7 @@ def aod_latlon(adfobj): base_name = adfobj.data.ref_case_label # Gather reference variable data - ds_base = adfobj.data.load_reference_climo_da(base_name, var) + ds_base = adfobj.data.load_reference_climo_da(var) if ds_base is None: dmsg = f"\t WARNING: No baseline climo file for {base_name} for variable `{var}`, global lat/lon plots skipped." adfobj.debug_log(dmsg) @@ -921,4 +1006,4 @@ def regrid_to_obs(adfobj, model_arr, obs_arr): ####### ############## -#END OF SCRIPT \ No newline at end of file +#END OF SCRIPT diff --git a/scripts/plotting/global_mean_timeseries_lnd.py b/scripts/plotting/global_mean_timeseries_lnd.py new file mode 100644 index 000000000..fde2b4e49 --- /dev/null +++ b/scripts/plotting/global_mean_timeseries_lnd.py @@ -0,0 +1,330 @@ +"""Use time series files to produce global mean time series plots for ADF web site. + +Includes a minimal Class for bringing CESM2 LENS data +from I. Simpson's directory (to be generalized). + +""" + +from pathlib import Path +from types import NoneType +import warnings # use to warn user about missing files. +import xarray as xr +import matplotlib.pyplot as plt +import plotting_functions as pf + + +def my_formatwarning(msg, *args, **kwargs): + """custom warning""" + # ignore everything except the message + return str(msg) + "\n" + + +warnings.formatwarning = my_formatwarning + + +def global_mean_timeseries_lnd(adfobj): + """ + load time series file, calculate global mean, annual mean + for each case + Make a combined plot, save it, add it to website. + Include the CESM2 LENS result if it can be found. + """ + + #Notify user that script has started: + print("\n Generating global mean time series plots...") + + # Gather ADF configurations + plot_loc = get_plot_loc(adfobj) + plot_type = adfobj.read_config_var("diag_basic_info").get("plot_type", "png") + res = adfobj.variable_defaults # will be dict of variable-specific plot preferences + # or an empty dictionary if use_defaults was not specified in YAML. + + # Loop over variables + for field in adfobj.diag_var_list: + + # Check res for any variable specific options that need to be used BEFORE going to the plot: + if field in res: + vres = res[field] + #If found then notify user, assuming debug log is enabled: + adfobj.debug_log(f"global_mean_timeseries: Found variable defaults for {field}") + #Extract category (if available): + web_category = vres.get("category", None) + else: + vres = {} + web_category = None + + # Extract variables: + # including a simpler way to get a dataset timeseries + baseline_name = adfobj.get_baseline_info("cam_case_name", required=True) + input_ts_baseline = Path(adfobj.get_baseline_info("cam_ts_loc", required=True)) + # TODO hard wired for single case name: + case_name = adfobj.get_cam_info("cam_case_name", required=True)[0] + input_ts_case = Path(adfobj.get_cam_info("cam_ts_loc", required=True)[0]) + + #Create list of time series files present for variable: + baseline_ts_filenames = f'{baseline_name}.*.{field}.*nc' + baseline_ts_files = sorted(input_ts_baseline.glob(baseline_ts_filenames)) + case_ts_filenames = f'{case_name}.*.{field}.*nc' + case_ts_files = sorted(input_ts_case.glob(case_ts_filenames)) + + ref_ts_ds = pf.load_dataset(baseline_ts_files) + weights = ref_ts_ds.landfrac * ref_ts_ds.area + ref_ts_da= ref_ts_ds[field] + + c_ts_ds = pf.load_dataset(case_ts_files) + c_weights = c_ts_ds.landfrac * c_ts_ds.area + c_ts_da= c_ts_ds[field] + + #Extract category (if available): + web_category = vres.get("category", None) + + # get variable defaults + scale_factor = vres.get('scale_factor', 1) + scale_factor_table = vres.get('scale_factor_table', 1) + add_offset = vres.get('add_offset', 0) + avg_method = vres.get('avg_method', 'mean') + + if avg_method == 'mean': + weights = weights/weights.sum() + c_weights = c_weights/c_weights.sum() + + # get units for variable + ref_ts_da.attrs['units'] = vres.get("new_unit", ref_ts_da.attrs.get('units', 'none')) + ref_ts_da.attrs['units'] = vres.get("table_unit", ref_ts_da.attrs.get('units', 'none')) + ref_ts_da.attrs['units'] = vres.get("ts_unit", ref_ts_da.attrs.get('units', 'none')) + units = ref_ts_da.attrs['units'] + + # scale for plotting, if needed + ref_ts_da = ref_ts_da * scale_factor * scale_factor_table + ref_ts_da.attrs['units'] = units + c_ts_da = c_ts_da * scale_factor * scale_factor_table + c_ts_da.attrs['units'] = units + + # Check to see if this field is available + if ref_ts_da is None: + print( + f"\t Variable named {field} provides Nonetype. Skipping this variable" + ) + validate_dims = True + else: + validate_dims = False + # reference time series global average + # TODO, make this more general for land? + ref_ts_da_ga = pf.spatial_average_lnd(ref_ts_da, weights=weights) + c_ts_da_ga = pf.spatial_average_lnd(c_ts_da, weights=c_weights) + + # annually averaged + ref_ts_da = pf.annual_mean(ref_ts_da_ga, whole_years=True, time_name="time") + c_ts_da = pf.annual_mean(c_ts_da_ga, whole_years=True, time_name="time") + + # make cumulative sum plots for NBP + if avg_method == 'cumsum': + c_ts_da = c_ts_da.cumsum() + ref_ts_da = ref_ts_da.cumsum() + + # check if variable has a lev dimension + has_lev_ref = pf.zm_validate_dims(ref_ts_da)[1] + if has_lev_ref: + print( + f"Variable named {field} has a lev dimension, which does not work with this script." + ) + continue + + ## SPECIAL SECTION -- CESM2 LENS DATA: + lens2_data = Lens2Data( + field + ) # Provides access to LENS2 dataset when available (class defined below) + + # Loop over model cases: + case_ts = {} # dictionary of annual mean, global mean time series + # use case nicknames instead of full case names if supplied: + labels = { + case_name: nickname if nickname else case_name + for nickname, case_name in zip( + adfobj.data.test_nicknames, adfobj.data.case_names + ) + } + ref_label = ( + adfobj.data.ref_nickname + if adfobj.data.ref_nickname + else adfobj.data.ref_case_label + ) + + skip_var = False + for case_name in adfobj.data.case_names: + #c_ts_da = adfobj.data.load_timeseries_da(case_name, field) + + if c_ts_da is None: + print( + f"\t Variable named {field} provides Nonetype. Skipping this variable" + ) + skip_var = True + continue + + # If no reference, we still need to check if this is a "2-d" varaible: + if validate_dims: + has_lat_ref, has_lev_ref = pf.zm_validate_dims(c_ts_da) + # End if + + # If 3-d variable, notify user, flag and move to next test case + if has_lev_ref: + print( + f"Variable named {field} has a lev dimension for '{case_name}', which does not work with this script." + ) + + skip_var = True + continue + # End if + + # Gather spatial avg for test case This seems redundant, since it was done above? + # Just try using c_ts_da directly, instead of doing the calculation again... + case_ts[labels[case_name]] = c_ts_da + + # End loop over cases + # If this case is 3-d or missing variable, then break the loop and go to next variable + if skip_var: + continue + + # Plot the timeseries + fig, ax = make_plot( + case_ts, lens2_data, label=adfobj.data.ref_nickname, ref_ts_da=ref_ts_da + ) + + ax.set_ylabel(getattr(ref_ts_da,"unit", units)) # add units + plot_name = plot_loc / f"{field}_GlobalMean_ANN_TimeSeries_Mean.{plot_type}" + + conditional_save(adfobj, plot_name, fig) + + adfobj.add_website_data( + plot_name, + f"{field}_GlobalMean", + None, + season="ANN", + multi_case=True, + plot_type="TimeSeries", + category=web_category, + ) + + #Notify user that script has ended: + print(" ... global mean time series plots have been generated successfully.") + + +# Helper/plotting functions +########################### + +def conditional_save(adfobj, plot_name, fig, verbose=None): + """Determines whether to save figure""" + # double check this + if adfobj.get_basic_info("redo_plot") and plot_name.is_file(): + # Case 1: Delete old plot, save new plot + plot_name.unlink() + fig.savefig(plot_name) + elif (adfobj.get_basic_info("redo_plot") and not plot_name.is_file()) or ( + not adfobj.get_basic_info("redo_plot") and not plot_name.is_file() + ): + # Save new plot + fig.savefig(plot_name) + elif not adfobj.get_basic_info("redo_plot") and plot_name.is_file(): + # Case 2: Keep old plot, do not save new plot + if verbose: + print("plot file detected, redo is false, so keep existing file.") + else: + warnings.warn( + f"Conditional save found unknown condition. File will not be written: {plot_name}" + ) + plt.close(fig) +###### + + +def get_plot_loc(adfobj, verbose=None): + """Return the path for plot files. + Contains side-effect: will make the directory and parents if needed. + """ + plot_location = adfobj.plot_location + if not plot_location: + plot_location = adfobj.get_basic_info("cam_diag_plot_loc") + if isinstance(plot_location, list): + for pl in plot_location: + plpth = Path(pl) + # Check if plot output directory exists, and if not, then create it: + if not plpth.is_dir(): + if verbose: + print(f"\t {pl} not found, making new directory") + plpth.mkdir(parents=True) + if len(plot_location) == 1: + plot_loc = Path(plot_location[0]) + else: + if verbose: + print( + f"Ambiguous plotting location since all cases go on same plot. Will put them in first location: {plot_location[0]}" + ) + plot_loc = Path(plot_location[0]) + else: + plot_loc = Path(plot_location) + print(f"Determined plot location: {plot_loc}") + return plot_loc +###### + + +class Lens2Data: + """Access Isla's LENS2 data to get annual means.""" + + def __init__(self, field): + self.field = field + self.has_lens, self.lens2 = self._include_lens() + + def _include_lens(self): + lens2_fil = Path( + f"/glade/campaign/cgd/cas/islas/CESM_DATA/LENS2/global_means/annualmeans/{self.field}_am_LENS2_first50.nc" + ) + if lens2_fil.is_file(): + lens2 = xr.open_mfdataset(lens2_fil) + has_lens = True + else: + warnings.warn(f"Time Series: Did not find LENS2 file for {self.field}.") + has_lens = False + lens2 = None + return has_lens, lens2 +###### + + +def make_plot(case_ts, lens2, label=None, ref_ts_da=None): + """plot yearly values of ref_ts_da""" + field = lens2.field # this will be defined even if no LENS2 data + fig, ax = plt.subplots() + + # Plot reference/baseline if available + if type(ref_ts_da) != NoneType: + ax.plot(ref_ts_da.year, ref_ts_da, label=label) + for c, cdata in case_ts.items(): + ax.plot(cdata.year, cdata, label=c) + if lens2.has_lens: + lensmin = lens2.lens2[field].min("M") # note: "M" is the member dimension + lensmax = lens2.lens2[field].max("M") + ax.fill_between(lensmin.year, lensmin, lensmax, color="lightgray", alpha=0.5) + ax.plot( + lens2.lens2[field].year, + lens2.lens2[field].mean("M"), + color="darkgray", + linewidth=2, + label="LENS2", + ) + # Get the current y-axis limits + ymin, ymax = ax.get_ylim() + # Check if the y-axis crosses zero + if ymin < 0 < ymax: + ax.axhline(y=0, color="lightgray", linestyle="-", linewidth=1) + ax.set_title(field, loc="left") + ax.set_xlabel("YEAR") + # Place the legend + ax.legend( + bbox_to_anchor=(0.5, -0.15), loc="upper center", ncol=min(len(case_ts), 3) + ) + plt.tight_layout(pad=2, w_pad=1.0, h_pad=1.0) + + return fig, ax +###### + + +############## +#END OF SCRIPT diff --git a/scripts/plotting/polar_map.py b/scripts/plotting/polar_map.py index dbcfcff70..8d87029e4 100644 --- a/scripts/plotting/polar_map.py +++ b/scripts/plotting/polar_map.py @@ -25,11 +25,25 @@ def polar_map(adfobj): # var_list = adfobj.diag_var_list model_rgrid_loc = adfobj.get_basic_info("cam_regrid_loc", required=True) + #model_rgrid_locs = adfobj.get_cam_info("cam_climo_regrid_loc", required=True) #Special ADF variable which contains the output paths for #all generated plots and tables for each case: plot_locations = adfobj.plot_location + kwargs = {} + + # + unstruct_plotting = adfobj.unstructured_plotting + print("unstruct_plotting", unstruct_plotting) + if unstruct_plotting: + kwargs["unstructured_plotting"] = unstruct_plotting + #mesh_file = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'#adfobj.mesh_file + #kwargs["mesh_file"] = mesh_file + else: + unstructured=False + print("kwargs", kwargs) + #CAM simulation variables (this is always assumed to be a list): case_names = adfobj.get_cam_info("cam_case_name", required=True) @@ -58,6 +72,7 @@ def polar_map(adfobj): data_name = adfobj.get_baseline_info("cam_case_name", required=True) # does not get used, is just here as a placemarker data_list = [data_name] # gets used as just the name to search for climo files HAS TO BE LIST data_loc = model_rgrid_loc #Just use the re-gridded model data path + #data_loc = Path(adfobj.get_baseline_info("cam_climo_regrid_loc", required=True)) #End if #Grab baseline years (which may be empty strings if using Obs): @@ -68,6 +83,12 @@ def polar_map(adfobj): test_nicknames = adfobj.case_nicknames["test_nicknames"] base_nickname = adfobj.case_nicknames["base_nickname"] + comp = adfobj.model_component + if comp == "atm": + hemis = ["NHPolar", "SHPolar"] + if comp == "lnd": + hemis = ["Arctic"] + res = adfobj.variable_defaults # will be dict of variable-specific plot preferences # or an empty dictionary if use_defaults was not specified in YAML. @@ -152,22 +173,32 @@ def polar_map(adfobj): # load data (observational) commparison files (we should explore intake as an alternative to having this kind of repeated code): if adfobj.compare_obs: - #For now, only grab one file (but convert to list for use below) - oclim_fils = [dclimo_loc] #Set data name: data_name = data_src + + if unstruct_plotting: + mesh_file = adfobj.mesh_files["baseline_mesh_file"] + kwargs["mesh_file"] = mesh_file + odata = adfobj.data.load_reference_climo_da(data_var, **kwargs) + #if ('ncol' in odata.dims) or ('lndgrid' in odata.dims): + if 1==1: + unstruct_base = True + odataset = adfobj.data.load_reference_climo_dataset(data_var, **kwargs) + area = odataset.area.isel(time=0) + landfrac = odataset.landfrac.isel(time=0) + # calculate weights + wgt_base = area * landfrac / (area * landfrac).sum() else: - oclim_fils = sorted(dclimo_loc.glob(f"{data_src}_{var}_baseline.nc")) - - oclim_ds = pf.load_dataset(oclim_fils) - if oclim_ds is None: + odata = adfobj.data.load_reference_regrid_da(data_var, **kwargs) + if odata is None: print("\t WARNING: Did not find any regridded reference climo files. Will try to skip.") print(f"\t INFO: Data Location, dclimo_loc is {dclimo_loc}") - print(f"\t The glob is: {data_src}_{var}_*.nc") + print(f"\t The glob is: {data_src}_{data_var}_*.nc") continue #Loop over model cases: for case_idx, case_name in enumerate(case_names): + #mclimo_rg_loc = Path(model_rgrid_locs[case_idx]) #Set case nickname: case_nickname = test_nicknames[case_idx] @@ -180,40 +211,55 @@ def polar_map(adfobj): print(f" {plot_loc} not found, making new directory") plot_loc.mkdir(parents=True) - # load re-gridded model files: - mclim_fils = sorted(mclimo_rg_loc.glob(f"{data_src}_{case_name}_{var}_*.nc")) + if unstruct_plotting: + mesh_file = adfobj.mesh_files["test_mesh_file"][case_idx] + kwargs["mesh_file"] = mesh_file + mdata = adfobj.data.load_climo_da(case_name, var, **kwargs) + #if ('ncol' in mdata.dims) or ('lndgrid' in mdata.dims): + if 1==1: + unstruct_case = True + mdataset = adfobj.data.load_climo_dataset(case_name, var, **kwargs) + area = mdataset.area.isel(time=0) + landfrac = mdataset.landfrac.isel(time=0) + # calculate weights + wgt = area * landfrac / (area * landfrac).sum() + else: + mdata = adfobj.data.load_regrid_da(case_name, var, **kwargs) - mclim_ds = pf.load_dataset(mclim_fils) - if mclim_ds is None: + if mdata is None: print("\t WARNING: Did not find any regridded test climo files. Will try to skip.") print(f"\t INFO: Data Location, mclimo_rg_loc, is {mclimo_rg_loc}") print(f"\t The glob is: {data_src}_{case_name}_{var}_*.nc") continue #End if - #Extract variable of interest - odata = oclim_ds[data_var].squeeze() # squeeze in case of degenerate dimensions - mdata = mclim_ds[var].squeeze() - - # APPLY UNITS TRANSFORMATION IF SPECIFIED: - # NOTE: looks like our climo files don't have all their metadata - mdata = mdata * vres.get("scale_factor",1) + vres.get("add_offset", 0) - # update units - mdata.attrs['units'] = vres.get("new_unit", mdata.attrs.get('units', 'none')) - - # Do the same for the baseline case if need be: - if not adfobj.compare_obs: - odata = odata * vres.get("scale_factor",1) + vres.get("add_offset", 0) - # update units - odata.attrs['units'] = vres.get("new_unit", odata.attrs.get('units', 'none')) - # or for observations. - else: - odata = odata * vres.get("obs_scale_factor",1) + vres.get("obs_add_offset", 0) - # Note: assume obs are set to have same untis as model. + if unstruct_plotting: + has_dims = {} + if len(wgt.n_face) == len(wgt_base.n_face): + vres["wgt"] = wgt + has_dims = {} + has_dims['has_lev'] = False + else: + print("The weights are different between test and baseline. Won't continue, eh.") + return + + if (not unstruct_case) and (unstruct_base): + print("Base is unstructured but Test is lat/lon. Can't continue?") + return + if (unstruct_case) and (not unstruct_base): + print("Base is lat/lon but Test is unstructured. Can't continue?") + return + if (unstruct_case) and (unstruct_base): + unstructured=True + if (not unstruct_case) and (not unstruct_base): + unstructured=False #Determine dimensions of variable: has_dims = pf.lat_lon_validate_dims(odata) - if has_dims: + has_lat_ref, has_lev_ref = pf.zm_validate_dims(odata) + has_lat, has_lev = pf.zm_validate_dims(mdata) + #if has_dims: + if (not has_lev) and (not has_lev_ref): #If observations/baseline CAM have the correct #dimensions, does the input CAM run have correct #dimensions as well? @@ -221,7 +267,8 @@ def polar_map(adfobj): #If both fields have the required dimensions, then #proceed with plotting: - if has_dims_cam: + #if has_dims_cam: + if 2==2: # # Seasonal Averages @@ -247,12 +294,9 @@ def polar_map(adfobj): # percent change pseasons[s] = (mseasons[s] - oseasons[s]) / np.abs(oseasons[s]) * 100.0 # relative change pseasons[s].attrs['units'] = '%' - #check if pct has NaN's or Inf values and if so set them to 0 to prevent plotting errors - pseasons[s] = pseasons[s].where(np.isfinite(pseasons[s]), np.nan) - pseasons[s] = pseasons[s].fillna(0.0) # make plots: northern and southern hemisphere separately: - for hemi_type in ["NHPolar", "SHPolar"]: + for hemi_type in hemis: #Create plot name and path: plot_name = plot_loc / f"{var}_{s}_{hemi_type}_Mean.{plot_type}" @@ -278,21 +322,38 @@ def polar_map(adfobj): # *Any other entries will be ignored. # NOTE: If we were doing all the plotting here, we could use whatever we want from the provided YAML file. - #Determine hemisphere to plot based on plot file name: - if hemi_type == "NHPolar": - hemi = "NH" - else: - hemi = "SH" - #End if - - pf.make_polar_plot(plot_name, case_nickname, base_nickname, - [syear_cases[case_idx],eyear_cases[case_idx]], - [syear_baseline,eyear_baseline], - mseasons[s], oseasons[s], dseasons[s], pseasons[s], hemisphere=hemi, obs=obs, **vres) + if comp == "atm": + #Determine hemisphere to plot based on plot file name: + if hemi_type == "NHPolar": + hemi = "NH" + else: + hemi = "SH" + #End if + if comp == "lnd": + hemi = hemi_type + + # Exclude certain plots, this may get difficult + if (var == 'GRAINC_TO_FOOD'): + print("\t\t Skipping 'GRAINC_TO_FOOD' polar plots") + continue + elif (var == 'FAREA_BURNED') and (s == 'SON'): + print("\t\t Skipping FAREA_BURNED in SON plot") + continue + # not working for regular grids? + elif (var == 'NPP'): + print("\t\t Skipping NPP polar plot") + continue + else: + pf.make_polar_plot(plot_name, case_nickname, base_nickname, + [syear_cases[case_idx],eyear_cases[case_idx]], + [syear_baseline,eyear_baseline], + mseasons[s], oseasons[s], dseasons[s], pseasons[s], + hemisphere=hemi, obs=obs, unstructured=unstructured, + **vres) - #Add plot to website (if enabled): - adfobj.add_website_data(plot_name, var, case_name, category=web_category, - season=s, plot_type=hemi_type) + #Add plot to website (if enabled): + adfobj.add_website_data(plot_name, var, case_name, category=web_category, + season=s, plot_type=hemi_type) else: #mdata dimensions check print(f"\t WARNING: skipping polar map for {var} as it doesn't have only lat/lon dims.") @@ -303,23 +364,23 @@ def polar_map(adfobj): #Check that case inputs have the correct dimensions (including "lev"): has_lat, has_lev = pf.zm_validate_dims(mdata) # assumes will work for both mdata & odata - # check if there is a lat dimension: + """# check if there is a lat dimension: if not has_lat: print( f"\t WARNING: Variable {var} is missing a lat dimension for '{case_name}', cannot continue to plot." ) continue - # End if + # End if""" #Check that case inputs have the correct dimensions (including "lev"): has_lat_ref, has_lev_ref = pf.zm_validate_dims(odata) - # check if there is a lat dimension: + """# check if there is a lat dimension: if not has_lat_ref: print( f"\t WARNING: Variable {var} is missing a lat dimension for '{data_name}', cannot continue to plot." ) - continue + continue""" #Check if both cases have vertical levels to continue if (has_lev) and (has_lev_ref): @@ -331,7 +392,7 @@ def polar_map(adfobj): #exists in the model data, which should already #have been interpolated to the standard reference #pressure levels: - if not (pres in mclim_ds['lev']): + if not (pres in mdata['lev']): #Move on to the next pressure level: print(f"\t WARNING: plot_press_levels value '{pres}' not a standard reference pressure, so skipping.") continue @@ -355,11 +416,15 @@ def polar_map(adfobj): pseasons[s] = (mseasons[s] - oseasons[s]) / abs(oseasons[s]) * 100.0 # relative change pseasons[s].attrs['units'] = '%' #check if pct has NaN's or Inf values and if so set them to 0 to prevent plotting errors - pseasons[s] = pseasons[s].where(np.isfinite(pseasons[s]), np.nan) - pseasons[s] = pseasons[s].fillna(0.0) + #pseasons[s] = pseasons[s].where(np.isfinite(pseasons[s]), np.nan) + #pseasons[s] = pseasons[s].fillna(0.0) # make plots: northern and southern hemisphere separately: - for hemi_type in ["NHPolar", "SHPolar"]: + for hemi_type in hemis: + print("mseasons[s].shape",mseasons[s].shape) + print("oseasons[s].shape",oseasons[s].shape) + print("dseasons[s].shape",dseasons[s].shape) + print("pseasons[s].shape",pseasons[s].shape) #Create plot name and path: plot_name = plot_loc / f"{var}_{pres}hpa_{s}_{hemi_type}_Mean.{plot_type}" @@ -386,17 +451,22 @@ def polar_map(adfobj): # *Any other entries will be ignored. # NOTE: If we were doing all the plotting here, we could use whatever we want from the provided YAML file. - #Determine hemisphere to plot based on plot file name: - if hemi_type == "NHPolar": - hemi = "NH" - else: - hemi = "SH" - #End if + if comp == "atm": + #Determine hemisphere to plot based on plot file name: + if hemi_type == "NHPolar": + hemi = "NH" + else: + hemi = "SH" + #End if + if comp == "lnd": + hemi = hemi_type pf.make_polar_plot(plot_name, case_nickname, base_nickname, [syear_cases[case_idx],eyear_cases[case_idx]], [syear_baseline,eyear_baseline], - mseasons[s], oseasons[s], dseasons[s], pseasons[s], hemisphere=hemi, obs=obs, **vres) + mseasons[s], oseasons[s], dseasons[s], pseasons[s], + hemisphere=hemi, obs=obs, unstructured=unstructured, + **vres) #Add plot to website (if enabled): adfobj.add_website_data(plot_name, f"{var}_{pres}hpa", @@ -423,4 +493,4 @@ def polar_map(adfobj): #END OF `polar_map` function ############## -# END OF FILE \ No newline at end of file +# END OF FILE diff --git a/scripts/plotting/regional_climatology.py b/scripts/plotting/regional_climatology.py new file mode 100644 index 000000000..0d4e94a60 --- /dev/null +++ b/scripts/plotting/regional_climatology.py @@ -0,0 +1,504 @@ +from pathlib import Path +import numpy as np +import yaml +import xarray as xr +import uxarray as ux +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import warnings # use to warn user about missing files. + +def my_formatwarning(msg, *args, **kwargs): + """custom warning""" + # ignore everything except the message + return str(msg) + "\n" + + +warnings.formatwarning = my_formatwarning + + +def regional_climatology(adfobj): + + """ + load climo file, subset for each region and each var + Make a combined plot, save it, add it to website. + + NOTES (from Meg): There are still a lot of to-do's with this script! + - convert region defintion netCDF file to a yml, read that in instead + - increase number of variables that have a climo plotted; i've just + added two, but left room for more in the subplots + - check that all varaibles have climo files; likely to break otherwise + - add option so that this works with a structured grid too # ...existing code... + if found: + #Check if observations dataset name is specified: + if "obs_name" in default_var_dict: + obs_name = default_var_dict["obs_name"] + else: + obs_name = obs_file_path.name + + if "obs_var_name" in default_var_dict: + obs_var_name = default_var_dict["obs_var_name"] + else: + obs_var_name = field + + # Use the resolved obs_file_path, not the original string + obs_data[field] = xr.open_mfdataset([str(obs_file_path)], combine="by_coords") + plot_obs[field] = True + # ...existing code... + - make sure that climo's are being plotted with the preferred units + - add in observations (need to regrid/area weight) + - need to figure out how to display the figures on the website + + """ + + #Notify user that script has started: + print("\n --- Generating regional climatology plots... ---") + + # Gather ADF configurations + # plot_loc = adfobj.get_basic_info('cam_diag_plot_loc') + # plot_type = adfobj.read_config_var("diag_basic_info").get("plot_type", "png") + plot_locations = adfobj.plot_location + plot_type = adfobj.get_basic_info('plot_type') + if not plot_type: + plot_type = 'png' + #res = adfobj.variable_defaults # will be dict of variable-specific plot preferences + # or an empty dictionary if use_defaults was not specified in YAML. + + # check if existing plots need to be redone + redo_plot = adfobj.get_basic_info('redo_plot') + print(f"\t NOTE: redo_plot is set to {redo_plot}") + + unstruct_plotting = adfobj.unstructured_plotting + print(f"\t unstruct_plotting", unstruct_plotting) + + case_nickname = adfobj.get_cam_info('case_nickname') + base_nickname = adfobj.get_baseline_info('case_nickname') + + region_list = adfobj.region_list + #TODO, make it easier for users decide on these? + regional_climo_var_list = ['TSA','PREC','ELAI', + 'FSDS','FLDS','SNOWDP','ASA', + 'FSH','QRUNOFF_TO_COUPLER','ET','FCTR', + 'GPP','TWS','SOILWATER_10CM','FAREA_BURNED', + ] + + # Extract variables: + baseline_name = adfobj.get_baseline_info("cam_case_name", required=True) + input_climo_baseline = Path(adfobj.get_baseline_info("cam_climo_loc", required=True)) + # TODO hard wired for single case name: + case_name = adfobj.get_cam_info("cam_case_name", required=True)[0] + input_climo_case = Path(adfobj.get_cam_info("cam_climo_loc", required=True)[0]) + + # Get grid file + mesh_file = adfobj.mesh_files["baseline_mesh_file"] + uxgrid = ux.open_grid(mesh_file) + + # Set keywords + kwargs = {} + kwargs["mesh_file"] = mesh_file + kwargs["unstructured_plotting"] = unstruct_plotting + + #Determine local directory: + _adf_lib_dir = adfobj.get_basic_info("obs_data_loc") + + #Determine whether to use adf defaults or custom: + _defaults_file = adfobj.get_basic_info('defaults_file') + # Note this won't work if no defaults_file is given + if _defaults_file is None: + _defaults_file = _adf_lib_dir/'adf_variable_defaults.yaml' + else: + print(f"\n\t Not using ADF default variables yaml file, instead using {_defaults_file}\n") + #End if + + #Open YAML file: + with open(_defaults_file, encoding='UTF-8') as dfil: + adfobj.__variable_defaults = yaml.load(dfil, Loader=yaml.SafeLoader) + + _variable_defaults = adfobj.__variable_defaults + + ## Read regions from yml file: + ymlFilename = adfobj.get_basic_info("regions_file") + with open(ymlFilename, 'r') as file: + regions = yaml.safe_load(file) + + #----------------------------------------- + #Extract the "obs_data_loc" default observational data location: + obs_data_loc = adfobj.get_basic_info("obs_data_loc") + + base_data = {} + case_data = {} + obs_data = {} + obs_name = {} + obs_var_name = {} + plot_obs = {} + + var_obs_dict = adfobj.var_obs_dict + + # First, load all variable data once (instead of inside nested loops) + for field in regional_climo_var_list: + # Load the global climatology for this variable + # TODO unit conversions are not handled consistently here + base_data[field] = adfobj.data.load_reference_climo_da(field, **kwargs) + case_data[field] = adfobj.data.load_climo_da(case_name, field, **kwargs) + + if type(base_data[field]) is type(None): + print('Missing file for ', field) + continue + else: + # get area and landfrac for base and case climo datasets + mdataset = adfobj.data.load_climo_dataset(case_name, field, **kwargs) + area_c = mdataset.area.isel(time=0) # drop time dimension to avoid confusion + landfrac_c = mdataset.landfrac.isel(time=0) + # Redundant, but we'll do this for consistency: + # TODO, won't handle loadling the basecase this way + #area_b = adfobj.data.load_reference_climo_da(baseline_name, 'area', **kwargs) + #landfrac_b = adfobj.data.load_reference_climo_da(baseline_name, 'landfrac', **kwargs) + + mdataset_base = adfobj.data.load_reference_climo_dataset(field, **kwargs) + area_b = mdataset_base.area.isel(time=0) + landfrac_b = mdataset_base.landfrac.isel(time=0) + + # calculate weights + # WW: 1) should actual weight calculation be done after subsetting to region? + # 2) Does this work as intended for different resolutions? + # wgt = area * landfrac # / (area * landfrac).sum() + + #----------------------------------------- + # Now, check if observations are to be plotted for this variable + plot_obs[field] = False + if field in _variable_defaults: + # Extract variable-obs dictionary + default_var_dict = _variable_defaults[field] + + #Check if an observations file is specified: + if "obs_file" in default_var_dict: + #Set found variable: + found = False + + #Extract path/filename: + obs_file_path = Path(default_var_dict["obs_file"]) + + #Check if file exists: + if not obs_file_path.is_file(): + #If not, then check if it is in "obs_data_loc" + if obs_data_loc: + obs_file_path = Path(obs_data_loc)/obs_file_path + + if obs_file_path.is_file(): + found = True + + else: + #File was found: + found = True + #End if + + #If found, then set observations dataset and variable names: + if found: + #Check if observations dataset name is specified: + if "obs_name" in default_var_dict: + obs_name[field] = default_var_dict["obs_name"] + else: + #If not, then just use obs file name: + obs_name[field] = obs_file_path.name + + #Check if observations variable name is specified: + if "obs_var_name" in default_var_dict: + #If so, then set obs_var_name variable: + obs_var_name[field] = default_var_dict["obs_var_name"] + else: + #Assume observation variable name is the same as model variable: + obs_var_name[field] = field + #End if + #Finally read in the obs! + obs_data[field] = xr.open_mfdataset([default_var_dict["obs_file"]], combine="by_coords") + plot_obs[field] = True + # Special handling for some variables:, NOT A GOOD HACK! + # TODO: improve this! + if (field == 'ASA') and ('BRDALB' in obs_data[field].variables): + obs_data[field]['BRDALB'] = obs_data[field]['BRDALB'].swap_dims({'lsmlat':'lat','lsmlon':'lon'}) + + else: + #If not found, then print to log and skip variable: + msg = f'''Unable to find obs file '{default_var_dict["obs_file"]}' ''' + msg += f"for variable '{field}'." + adfobj.debug_log(msg) + continue + # End if + + else: + #No observation file was specified, so print to log and skip variable: + adfobj.debug_log(f"No observations file was listed for variable '{field}'.") + continue + else: + #Variable not in defaults file, so print to log and skip variable: + msg = f"Variable '{field}' not found in variable defaults file: `{_defaults_file}`" + adfobj.debug_log(msg) + # End if + # End of observation loading + #----------------------------------------- + + #----------------------------------------- + # Loop over regions for selected variable + for iReg in range(len(region_list)): + print(f"\n\t - Plotting regional climatology for: {region_list[iReg]}") + # regionDS_thisRg = regionDS.isel(region=region_indexList[iReg]) + box_west, box_east, box_south, box_north, region_category = get_region_boundaries(regions, region_list[iReg]) + ## Set up figure + ## TODO: Make the plot size/number of subplots resopnsive to number of fields specified + fig,axs = plt.subplots(4,4, figsize=(18,12)) + axs = axs.ravel() + + plt_counter = 1 + for field in regional_climo_var_list: + mdataset = adfobj.data.load_climo_dataset(case_name, field, **kwargs) + + if type(base_data[field]) is type(None): + continue + else: + if unstruct_plotting == True: + # uxarray output is time*nface, sum over nface + base_var,wgt_sub = getRegion_uxarray(uxgrid, base_data, field, area_b, landfrac_b, + box_west, box_east, + box_south, box_north) + base_var_wgtd = np.sum(base_var * wgt_sub, axis=-1) # WW not needed?/ np.sum(wgt_sub) + + case_var,wgt_sub = getRegion_uxarray(uxgrid, case_data, field, area_c, landfrac_c, + box_west, box_east, + box_south, box_north) + case_var_wgtd = np.sum(case_var * wgt_sub, axis=-1) #/ np.sum(wgt_sub) + + else: # regular lat/lon grid + # xarray output is time*lat*lon, sum over lat/lon + base_var, wgt_sub = getRegion_xarray(base_data[field], field, + box_west, box_east, + box_south, box_north, + area_b, landfrac_b) + base_var_wgtd = np.sum(base_var * wgt_sub, axis=(1,2)) + + case_var, wgt_sub = getRegion_xarray(case_data[field], field, + box_west, box_east, + box_south, box_north, + area_c, landfrac_c) + case_var_wgtd = np.sum(case_var * wgt_sub, axis=(1,2)) + + # Read in observations, if available + if plot_obs[field] == True: + # obs output is time*lat*lon, sum over lat/lon + obs_var, wgt_sub = getRegion_xarray(obs_data[field], field, + box_west, box_east, + box_south, box_north, + obs_var_name=obs_var_name[field]) + obs_var_wgtd = np.sum(obs_var * wgt_sub, axis=(1,2)) #/ np.sum(wgt_sub) + + ## Plot the map: + if plt_counter==1: + ## Define region in first subplot + fig.delaxes(axs[0]) + + transform = ccrs.PlateCarree() + projection = ccrs.PlateCarree() + base_var_mask = base_var.isel(time=0) + + if unstruct_plotting == True: + base_var_mask[np.isfinite(base_var_mask)]=1 + collection = base_var_mask.to_polycollection() + + collection.set_transform(transform) + collection.set_cmap('rainbow_r') + collection.set_antialiased(False) + map_ax = fig.add_subplot(4, 4, 1, projection=ccrs.PlateCarree()) + + map_ax.coastlines() + map_ax.add_collection(collection) + elif unstruct_plotting == False: + base_var_mask = base_var_mask.copy() + base_var_mask.values[np.isfinite(base_var_mask.values)] = 1 + + map_ax = fig.add_subplot(4, 4, 1, projection=ccrs.PlateCarree()) + map_ax.coastlines() + + # Plot using pcolormesh for structured grids + im = map_ax.pcolormesh(base_var_mask.lon, base_var_mask.lat, + base_var_mask.values, + transform=transform, + cmap='rainbow_r', + shading='auto') + + # Add map extent selection + if region_list[iReg]=='N Hemisphere Land': + map_ax.set_extent([-180, 179, -3, 90],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='Global': + map_ax.set_extent([-180, 179, -89, 90],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='S Hemisphere Land': + map_ax.set_extent([-180, 179, -89, 3],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='Polar': + map_ax.set_extent([-180, 179, 45, 90],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='CONUS': + map_ax.set_extent([-140, -55, 10, 70],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='S America': + map_ax.set_extent([-100, -20, -45, 20],crs=ccrs.PlateCarree()) + else: + if ((box_south >= 30) & (box_east<=-5) ): + map_ax.set_extent([-180, 0, 30, 90],crs=ccrs.PlateCarree()) + elif ((box_south >= 30) & (box_east>=-5) ): + map_ax.set_extent([-10, 179, 30, 90],crs=ccrs.PlateCarree()) + elif ((box_south <= 30) & (box_south >= -30) & + (box_east<=-5) ): + map_ax.set_extent([-180, 0, -30, 30],crs=ccrs.PlateCarree()) + elif ((box_south <= 30) & (box_south >= -30) & + (box_east>=-5) ): + map_ax.set_extent([-10, 179, -30, 30],crs=ccrs.PlateCarree()) + elif ((box_south <= -30) & (box_south >= -60) & + (box_east>=-5) ): + map_ax.set_extent([-10, 179, -89, -30],crs=ccrs.PlateCarree()) + elif ((box_south <= -30) & (box_south >= -60) & + (box_east<=-5) ): + map_ax.set_extent([-180, 0, -89, -30],crs=ccrs.PlateCarree()) + elif ((box_south <= -60)): + map_ax.set_extent([-180, 179, -89, -60],crs=ccrs.PlateCarree()) + # End if for plotting map extent + + ## Plot the climatology: + if type(base_data[field]) is type(None): + print('Missing file for ', field) + continue + else: + axs[plt_counter].plot(np.arange(12)+1, base_var_wgtd, + label=base_nickname, linewidth=2) + axs[plt_counter].plot(np.arange(12)+1, case_var_wgtd, + label=case_nickname, linewidth=2) + if plot_obs[field] == True: + axs[plt_counter].plot(np.arange(12)+1, obs_var_wgtd, + label=obs_name[field], color='black', linewidth=2) + axs[plt_counter].legend() + + axs[plt_counter].set_title(field) + axs[plt_counter].set_ylabel(base_data[field].units) + axs[plt_counter].set_xticks(np.arange(1, 13, 2)) + + + plt_counter = plt_counter+1 + + fig.subplots_adjust(hspace=0.3, wspace=0.3) + + # Save out figure + plot_loc = Path(plot_locations[0]) / f'{region_list[iReg]}_plot_RegionalClimo_Mean.{plot_type}' + + # Check redo_plot. If set to True: remove old plots, if they already exist: + if (not redo_plot) and plot_loc.is_file(): + #Add already-existing plot to website (if enabled): + adfobj.debug_log(f"'{plot_loc}' exists and clobber is false.") + adfobj.add_website_data(plot_loc, region_list[iReg], None, season=None, multi_case=True, + category=region_category, non_season=True, plot_type = "RegionalClimo") + + #Continue to next iteration: + return + elif (redo_plot): + if plot_loc.is_file(): + plot_loc.unlink() + + fig.savefig(plot_loc, bbox_inches='tight', facecolor='white') + plt.close() + + #Add plot to website (if enabled): + adfobj.add_website_data(plot_loc, region_list[iReg], None, season=None, multi_case=True, + non_season=True, category=region_category, plot_type = "RegionalClimo") + + return + +print("\n --- Regional climatology plots generated successfully! ---") + +def getRegion_uxarray(gridDS, varDS, varName, area, landfrac, BOX_W, BOX_E, BOX_S, BOX_N): + # Method 2: Filter mesh nodes based on coordinates + node_lons = gridDS.face_lon + node_lats = gridDS.face_lat + + # Create a boolean mask for nodes within your domain + in_domain = ((node_lons >= BOX_W) & (node_lons <= BOX_E) & + (node_lats >= BOX_S) & (node_lats <= BOX_N)) + + # Get the indices of nodes within your domain + node_indices = np.where(in_domain)[0] + + # Subset the dataset using these node indices + domain_subset = varDS[varName].isel(n_face=node_indices) + area_subset = area.isel(n_face=node_indices) + landfrac_subset = landfrac.isel(n_face=node_indices) + wgt_subset = area_subset * landfrac_subset / (area_subset* landfrac_subset).sum() + + return domain_subset,wgt_subset + +def getRegion_xarray(varDS, varName, + BOX_W, BOX_E, BOX_S, BOX_N, + area=None, landfrac=None, + obs_var_name=None): + # Assumes regular lat/lon grid in xarray Dataset + # Assumes varDS has 'lon' and 'lat' coordinates w/ lon in [0,360] + # Convert BOX_W and BOX_E to [0,360] if necessary + # Also assumes global weights have already been calculated & masked appropriately + if (BOX_W == -180) & (BOX_E == 180): + BOX_W, BOX_E = 0, 360 # Special case for global domain + if BOX_W < 0: BOX_W = BOX_W + 360 + if BOX_E < 0: BOX_E = BOX_E + 360 + + if varName not in varDS: + varName = obs_var_name + + if varDS.lon.values.min() < 0: + # Convert lon to [0,360] if necessary + longitude = varDS['lon'] + varDS = varDS.assign_coords(lon= (longitude + 180) % 360) + print(f"Converted lon to [0,360] for variable {varName}") + + # TODO is there a less brittle way to do this? + if (area is not None) and (landfrac is not None): + weight = area * landfrac + elif ('weight' in varDS) and ('datamask' in varDS): + weight = varDS['weight'] * varDS['datamask'] + elif ('weight' in varDS) and ('LANDFRAC' in varDS): + #used for MODIS albedo product + weight = varDS['weight'] * varDS['LANDFRAC'] + elif 'area' in varDS and 'landfrac' in varDS: + weight = varDS['area'] * varDS['landfrac'] + elif 'area' in varDS and 'landmask' in varDS: + weight = varDS['area'] * varDS['landmask'] + # Fluxnet data also has a datamask + if 'datamask' in varDS: + weight = weight * varDS['datamask'] + else: + raise ValueError("No valid weight, area, or landmask found in {varName} dataset.") + + # check we have a data array for the variable + if isinstance(varDS, xr.Dataset): + varDS = varDS[varName] + + # Subset the dataarray using the specified box + if BOX_W < BOX_E: + domain_subset = varDS.sel(lat=slice(BOX_S, BOX_N), + lon=slice(BOX_W, BOX_E)) + weight_subset = weight.sel(lat=slice(BOX_S, BOX_N), + lon=slice(BOX_W, BOX_E)) + + else: + # Use boolean indexing to select the region + # The parentheses are important due to operator precedence + west_of_0 = varDS.lon >= BOX_W + east_of_0 = varDS.lon <= BOX_E + domain_subset = varDS.sel(lat=slice(BOX_S, BOX_N), + lon=(west_of_0 | east_of_0)) + weight_subset = weight.sel(lat=slice(BOX_S, BOX_N), + lon=(west_of_0 | east_of_0)) + + wgt_subset = weight_subset / weight_subset.sum() + return domain_subset,wgt_subset + +def get_region_boundaries(regions, region_name): + """Get the boundaries of a specific region.""" + if region_name not in regions: + raise ValueError(f"Region '{region_name}' not found in regions dictionary") + + region = regions[region_name] + south, north = region['lat_bounds'] + west, east = region['lon_bounds'] + region_category = region['region_category'] if 'region_category' in region else None + + return west, east, south, north, region_category diff --git a/scripts/plotting/regional_timeseries.py b/scripts/plotting/regional_timeseries.py new file mode 100644 index 000000000..54b7a7e76 --- /dev/null +++ b/scripts/plotting/regional_timeseries.py @@ -0,0 +1,500 @@ +""" +Use time series files to produce regional mean time series plots for LDF web site. + +""" +from pathlib import Path +from types import NoneType +import numpy as np +import yaml +import xarray as xr +import uxarray as ux +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import plotting_functions as pf +import warnings # use to warn user about missing files. + +def my_formatwarning(msg, *args, **kwargs): + """custom warning""" + # ignore everything except the message + return str(msg) + "\n" + + +warnings.formatwarning = my_formatwarning + + +def regional_timeseries(adfobj): + + """ + load timeseries file, subset for each region and each var + calculate regional annual mean time series + Make a combined plot, save it, add it to website. + + TODO + - check that all varaibles have TS files; likely to break otherwise + if found: + #Check if observations dataset name is specified: + if "obs_name" in default_var_dict: + obs_name = default_var_dict["obs_name"] + else: + obs_name = obs_file_path.name + + if "obs_var_name" in default_var_dict: + obs_var_name = default_var_dict["obs_var_name"] + else: + obs_var_name = field + + # Use the resolved obs_file_path, not the original string + obs_data[field] = xr.open_mfdataset([str(obs_file_path)], combine="by_coords") + plot_obs[field] = True + # ...existing code... + - make sure that TS's are being plotted with the preferred units + + """ + + #Notify user that script has started: + print("\n --- Generating regional time series plots... ---") + + # Gather ADF configurations + # plot_loc = adfobj.get_basic_info('cam_diag_plot_loc') + # plot_type = adfobj.read_config_var("diag_basic_info").get("plot_type", "png") + plot_locations = adfobj.plot_location + plot_type = adfobj.get_basic_info('plot_type') + if not plot_type: + plot_type = 'png' + #res = adfobj.variable_defaults # will be dict of variable-specific plot preferences + # or an empty dictionary if use_defaults was not specified in YAML. + + # check if existing plots need to be redone + redo_plot = adfobj.get_basic_info('redo_plot') + print(f"\t NOTE: redo_plot is set to {redo_plot}") + + unstruct_plotting = adfobj.unstructured_plotting + print(f"\t unstruct_plotting", unstruct_plotting) + + case_nickname = adfobj.get_cam_info('case_nickname') + base_nickname = adfobj.get_baseline_info('case_nickname') + + region_list = adfobj.region_list + #TODO, make it easier for users decide on these by adding to yaml file + regional_ts_var_list = ['TSA','PREC','ELAI', + 'FSDS','FLDS','SNOWDP','ASA', + 'FSH','QRUNOFF_TO_COUPLER','ET','FCTR', + 'GPP','TWS','SOILWATER_10CM','FAREA_BURNED', + ] + + # Extract variables: + #baseline_name = adfobj.get_baseline_info("cam_case_name", required=True) + #input_ts_baseline = Path(adfobj.get_baseline_info("cam_ts_loc", required=True)) + # TODO hard wired for single case name: + case_name = adfobj.get_cam_info("cam_case_name", required=True)[0] + #input_ts_case = Path(adfobj.get_cam_info("cam_ts_loc", required=True)[0]) + + # Get grid file + mesh_file = adfobj.mesh_files["baseline_mesh_file"] + uxgrid = ux.open_grid(mesh_file) + + # Set keywords + kwargs = {} + kwargs["mesh_file"] = mesh_file + kwargs["unstructured_plotting"] = unstruct_plotting + + #Determine local directory: + _adf_lib_dir = adfobj.get_basic_info("obs_data_loc") + + #Determine whether to use adf defaults or custom: + _defaults_file = adfobj.get_basic_info('defaults_file') + # Note this won't work if no defaults_file is given + if _defaults_file is None: + _defaults_file = _adf_lib_dir/'adf_variable_defaults.yaml' + else: + print(f"\n\t Not using ADF default variables yaml file, instead using {_defaults_file}\n") + #End if + + #Open YAML file: + with open(_defaults_file, encoding='UTF-8') as dfil: + adfobj.__variable_defaults = yaml.load(dfil, Loader=yaml.SafeLoader) + + _variable_defaults = adfobj.__variable_defaults + + ## Read regions from yml file: + ymlFilename = adfobj.get_basic_info("regions_file") + with open(ymlFilename, 'r') as file: + regions = yaml.safe_load(file) + + #----------------------------------------- + #Extract the "obs_data_loc" default observational data location: + obs_data_loc = adfobj.get_basic_info("obs_data_loc") + + base_data = {} + case_data = {} + obs_data = {} + obs_name = {} + obs_var_name = {} + plot_obs = {} + + #var_obs_dict = adfobj.var_obs_dict + + # First, load all variable data once (instead of inside nested loops) + for field in regional_ts_var_list: + # Load the global time series for this variable + # TODO unit conversions are not handled consistently here + base_data[field] = adfobj.data.load_reference_timeseries_da(field, **kwargs) + case_data[field] = adfobj.data.load_timeseries_da(case_name, field, **kwargs) + + if type(base_data[field]) is type(None): + print('Missing file for ', field) + continue + else: + # get area and landfrac for base and case ts datasets + mdataset = adfobj.data.load_timeseries_dataset(case_name, field, **kwargs) + # TODO: check with for structured grids + area_c = mdataset.area #.isel(time=0) # drop time dimension to avoid confusion + landfrac_c = mdataset.landfrac #.isel(time=0) + + mdataset_base = adfobj.data.load_reference_timeseries_dataset(field, **kwargs) + area_b = mdataset_base.area#.isel(time=0) + landfrac_b = mdataset_base.landfrac#.isel(time=0) + + #----------------------------------------- + # Now, check if observations are to be plotted for this variable + plot_obs[field] = False + if field in _variable_defaults: + # Extract variable-obs dictionary + default_var_dict = _variable_defaults[field] + + #Check if an observations file is specified: + if "obs_file" in default_var_dict: + #Set found variable: + found = False + + #Extract path/filename: + obs_file_path = Path(default_var_dict["obs_file"]) + + #Check if file exists: + if not obs_file_path.is_file(): + #If not, then check if it is in "obs_data_loc" + if obs_data_loc: + obs_file_path = Path(obs_data_loc)/obs_file_path + + if obs_file_path.is_file(): + found = True + + else: + #File was found: + found = True + #End if + + #If found, then set observations dataset and variable names: + if found: + #Check if observations dataset name is specified: + if "obs_name" in default_var_dict: + obs_name[field] = default_var_dict["obs_name"] + else: + #If not, then just use obs file name: + obs_name[field] = obs_file_path.name + + #Check if observations variable name is specified: + if "obs_var_name" in default_var_dict: + #If so, then set obs_var_name variable: + obs_var_name[field] = default_var_dict["obs_var_name"] + else: + #Assume observation variable name is the same as model variable: + obs_var_name[field] = field + #End if + #Finally read in the obs! + obs_data[field] = xr.open_mfdataset([default_var_dict["obs_file"]], combine="by_coords") + plot_obs[field] = True + # Special handling for some variables:, NOT A GOOD HACK! + # TODO: improve this! + if (field == 'ASA') and ('BRDALB' in obs_data[field].variables): + obs_data[field]['BRDALB'] = obs_data[field]['BRDALB'].swap_dims({'lsmlat':'lat','lsmlon':'lon'}) + + else: + #If not found, then print to log and skip variable: + msg = f'''Unable to find obs file '{default_var_dict["obs_file"]}' ''' + msg += f"for variable '{field}'." + adfobj.debug_log(msg) + continue + # End if + + else: + #No observation file was specified, so print to log and skip variable: + adfobj.debug_log(f"No observations file was listed for variable '{field}'.") + continue + else: + #Variable not in defaults file, so print to log and skip variable: + msg = f"Variable '{field}' not found in variable defaults file: `{_defaults_file}`" + adfobj.debug_log(msg) + # End if + # End of observation loading + #----------------------------------------- + + #----------------------------------------- + # Loop over regions for selected variable + for iReg in range(len(region_list)): + print(f"\n\t - Plotting regional timeseries for: {region_list[iReg]}") + # regionDS_thisRg = regionDS.isel(region=region_indexList[iReg]) + box_west, box_east, box_south, box_north, region_category = get_region_boundaries(regions, region_list[iReg]) + ## Set up figure + ## TODO: Make the plot size/number of subplots resopnsive to number of fields specified + fig,axs = plt.subplots(4,4, figsize=(18,12)) + axs = axs.ravel() + + plt_counter = 1 + for field in regional_ts_var_list: + mdataset = adfobj.data.load_timeseries_dataset(case_name, field, **kwargs) + + if type(base_data[field]) is type(None): + continue + else: + if unstruct_plotting == True: + # uxarray output is time*nface, sum over nface + base_var,wgt_sub = getRegion_uxarray(uxgrid, base_data, field, area_b, landfrac_b, + box_west, box_east, + box_south, box_north) + base_var_wgtd = np.sum(base_var * wgt_sub, axis=-1) + + case_var,wgt_sub = getRegion_uxarray(uxgrid, case_data, field, area_c, landfrac_c, + box_west, box_east, + box_south, box_north) + case_var_wgtd = np.sum(case_var * wgt_sub, axis=-1) + + # annually averaged + base_var_ann = pf.annual_mean(base_var_wgtd, whole_years=True, time_name="time") + case_var_ann = pf.annual_mean(case_var_wgtd, whole_years=True, time_name="time") + + else: # regular lat/lon grid + # xarray output is time*lat*lon, sum over lat/lon + base_var, wgt_sub = getRegion_xarray(base_data[field], field, + box_west, box_east, + box_south, box_north, + area_b, landfrac_b) + base_var_wgtd = np.sum(base_var * wgt_sub, axis=(1,2)) + + case_var, wgt_sub = getRegion_xarray(case_data[field], field, + box_west, box_east, + box_south, box_north, + area_c, landfrac_c) + case_var_wgtd = np.sum(case_var * wgt_sub, axis=(1,2)) + + # annually averaged + base_var_ann = pf.annual_mean(base_var_wgtd, whole_years=True, time_name="time") + case_var_ann = pf.annual_mean(case_var_wgtd, whole_years=True, time_name="time") + + # Read in observations, if available + if plot_obs[field] == True: + # obs output is time*lat*lon, sum over lat/lon + obs_var, wgt_sub = getRegion_xarray(obs_data[field], field, + box_west, box_east, + box_south, box_north, + obs_var_name=obs_var_name[field]) + obs_var_wgtd = np.sum(obs_var * wgt_sub, axis=(1,2)) #/ np.sum(wgt_sub) + + ## Plot the map: + if plt_counter==1: + ## Define region in first subplot + fig.delaxes(axs[0]) + + transform = ccrs.PlateCarree() + projection = ccrs.PlateCarree() + base_var_mask = base_var.isel(time=0) + + if unstruct_plotting == True: + base_var_mask[np.isfinite(base_var_mask)]=1 + collection = base_var_mask.to_polycollection() + + collection.set_transform(transform) + collection.set_cmap('rainbow_r') + collection.set_antialiased(False) + map_ax = fig.add_subplot(4, 4, 1, projection=ccrs.PlateCarree()) + + map_ax.coastlines() + map_ax.add_collection(collection) + elif unstruct_plotting == False: + base_var_mask = base_var_mask.copy() + base_var_mask.values[np.isfinite(base_var_mask.values)] = 1 + + map_ax = fig.add_subplot(4, 4, 1, projection=ccrs.PlateCarree()) + map_ax.coastlines() + + # Plot using pcolormesh for structured grids + im = map_ax.pcolormesh(base_var_mask.lon, base_var_mask.lat, + base_var_mask.values, + transform=transform, + cmap='rainbow_r', + shading='auto') + + # Add map extent selection + if region_list[iReg]=='N Hemisphere Land': + map_ax.set_extent([-180, 179, -3, 90],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='Global': + map_ax.set_extent([-180, 179, -89, 90],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='S Hemisphere Land': + map_ax.set_extent([-180, 179, -89, 3],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='Polar': + map_ax.set_extent([-180, 179, 45, 90],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='CONUS': + map_ax.set_extent([-140, -55, 10, 70],crs=ccrs.PlateCarree()) + elif region_list[iReg]=='S America': + map_ax.set_extent([-100, -20, -45, 20],crs=ccrs.PlateCarree()) + else: + if ((box_south >= 30) & (box_east<=-5) ): + map_ax.set_extent([-180, 0, 30, 90],crs=ccrs.PlateCarree()) + elif ((box_south >= 30) & (box_east>=-5) ): + map_ax.set_extent([-10, 179, 30, 90],crs=ccrs.PlateCarree()) + elif ((box_south <= 30) & (box_south >= -30) & + (box_east<=-5) ): + map_ax.set_extent([-180, 0, -30, 30],crs=ccrs.PlateCarree()) + elif ((box_south <= 30) & (box_south >= -30) & + (box_east>=-5) ): + map_ax.set_extent([-10, 179, -30, 30],crs=ccrs.PlateCarree()) + elif ((box_south <= -30) & (box_south >= -60) & + (box_east>=-5) ): + map_ax.set_extent([-10, 179, -89, -30],crs=ccrs.PlateCarree()) + elif ((box_south <= -30) & (box_south >= -60) & + (box_east<=-5) ): + map_ax.set_extent([-180, 0, -89, -30],crs=ccrs.PlateCarree()) + elif ((box_south <= -60)): + map_ax.set_extent([-180, 179, -89, -60],crs=ccrs.PlateCarree()) + # End if for plotting map extent + + ## Plot the timeseries: + if type(base_data[field]) is type(None): + print('Missing file for ', field) + continue + else: + # WW set time axis to years + axs[plt_counter].plot(base_var_ann.year, base_var_ann, + label=base_nickname, linewidth=2) + axs[plt_counter].plot(case_var_ann.year, case_var_ann, + label=case_nickname, linewidth=2) + # TODO, reinstate obs plotting once obs TS files are available + # if plot_obs[field] == True: + # axs[plt_counter].plot(np.arange(12)+1, obs_var_wgtd, + # label=obs_name[field], color='black', linewidth=2) + axs[plt_counter].set_title(field) + axs[plt_counter].set_ylabel(base_data[field].units) + if plt_counter == 3 or plt_counter == 7 or plt_counter ==11 or plt_counter ==15: + axs[plt_counter].legend() + + plt_counter = plt_counter+1 + + fig.subplots_adjust(hspace=0.3, wspace=0.3) + + # Save out figure + plot_loc = Path(plot_locations[0]) / f'{region_list[iReg]}_plot_RegionalTimeSeries_Mean.{plot_type}' + + # Check redo_plot. If set to True: remove old plots, if they already exist: + if (not redo_plot) and plot_loc.is_file(): + #Add already-existing plot to website (if enabled): + adfobj.debug_log(f"'{plot_loc}' exists and clobber is false.") + adfobj.add_website_data(plot_loc, region_list[iReg], None, season=None, multi_case=True, + category=region_category, non_season=True, plot_type = "RegionalTimeSeries") + + #Continue to next iteration: + return + elif (redo_plot): + if plot_loc.is_file(): + plot_loc.unlink() + + fig.savefig(plot_loc, bbox_inches='tight', facecolor='white') + plt.close() + + #Add plot to website (if enabled): + adfobj.add_website_data(plot_loc, region_list[iReg], None, season=None, multi_case=True, + non_season=True, category=region_category, plot_type = "RegionalTimeSeries") + + return + +print("\n --- Regional time series plots generated successfully! ---") + +def getRegion_uxarray(gridDS, varDS, varName, area, landfrac, BOX_W, BOX_E, BOX_S, BOX_N): + # Method 2: Filter mesh nodes based on coordinates + node_lons = gridDS.face_lon + node_lats = gridDS.face_lat + + # Create a boolean mask for nodes within your domain + in_domain = ((node_lons >= BOX_W) & (node_lons <= BOX_E) & + (node_lats >= BOX_S) & (node_lats <= BOX_N)) + + # Get the indices of nodes within your domain + node_indices = np.where(in_domain)[0] + + # Subset the dataset using these node indices + domain_subset = varDS[varName].isel(n_face=node_indices) + area_subset = area.isel(n_face=node_indices) + landfrac_subset = landfrac.isel(n_face=node_indices) + wgt_subset = area_subset * landfrac_subset / (area_subset* landfrac_subset).sum() + + return domain_subset,wgt_subset + +def getRegion_xarray(varDS, varName, + BOX_W, BOX_E, BOX_S, BOX_N, + area=None, landfrac=None, + obs_var_name=None): + # Assumes regular lat/lon grid in xarray Dataset + # Assumes varDS has 'lon' and 'lat' coordinates w/ lon in [0,360] + # Convert BOX_W and BOX_E to [0,360] if necessary + # Also assumes global weights have already been calculated & masked appropriately + if (BOX_W == -180) & (BOX_E == 180): + BOX_W, BOX_E = 0, 360 # Special case for global domain + if BOX_W < 0: BOX_W = BOX_W + 360 + if BOX_E < 0: BOX_E = BOX_E + 360 + + if varName not in varDS: + varName = obs_var_name + + # TODO is there a less brittle way to do this? + if (area is not None) and (landfrac is not None): + weight = area * landfrac + elif ('weight' in varDS) and ('datamask' in varDS): + weight = varDS['weight'] * varDS['datamask'] + elif ('weight' in varDS) and ('LANDFRAC' in varDS): + #used for MODIS albedo product + weight = varDS['weight'] * varDS['LANDFRAC'] + elif 'area' in varDS and 'landfrac' in varDS: + weight = varDS['area'] * varDS['landfrac'] + elif 'area' in varDS and 'landmask' in varDS: + weight = varDS['area'] * varDS['landmask'] + # Fluxnet data also has a datamask + if 'datamask' in varDS: + weight = weight * varDS['datamask'] + else: + raise ValueError("No valid weight, area, or landmask found in {varName} dataset.") + + # check we have a data array for the variable + if isinstance(varDS, xr.Dataset): + varDS = varDS[varName] + + # Subset the dataarray using the specified box + if BOX_W < BOX_E: + domain_subset = varDS.sel(lat=slice(BOX_S, BOX_N), + lon=slice(BOX_W, BOX_E)) + weight_subset = weight.sel(lat=slice(BOX_S, BOX_N), + lon=slice(BOX_W, BOX_E)) + + else: + # Use boolean indexing to select the region + # The parentheses are important due to operator precedence + west_of_0 = varDS.lon >= BOX_W + east_of_0 = varDS.lon <= BOX_E + domain_subset = varDS.sel(lat=slice(BOX_S, BOX_N), + lon=(west_of_0 | east_of_0)) + weight_subset = weight.sel(lat=slice(BOX_S, BOX_N), + lon=(west_of_0 | east_of_0)) + + wgt_subset = weight_subset / weight_subset.sum() + weight_subset = weight.sel + return domain_subset,wgt_subset + +def get_region_boundaries(regions, region_name): + """Get the boundaries of a specific region.""" + if region_name not in regions: + raise ValueError(f"Region '{region_name}' not found in regions dictionary") + + region = regions[region_name] + south, north = region['lat_bounds'] + west, east = region['lon_bounds'] + region_category = region['region_category'] if 'region_category' in region else None + + return west, east, south, north, region_category diff --git a/scripts/regridding/regrid_and_vert_interp.py b/scripts/regridding/regrid_and_vert_interp.py index 8316cb3f1..44b8fb99d 100644 --- a/scripts/regridding/regrid_and_vert_interp.py +++ b/scripts/regridding/regrid_and_vert_interp.py @@ -1,4 +1,5 @@ #Import standard modules: +from pdb import lasti2lineno import xarray as xr def regrid_and_vert_interp(adf): @@ -36,6 +37,8 @@ def regrid_and_vert_interp(adf): from pathlib import Path + from adf_base import AdfError + # regridding # Try just using the xarray method # import xesmf as xe # This package is for regridding, and is just one potential solution. @@ -56,9 +59,24 @@ def regrid_and_vert_interp(adf): var_list = adf.diag_var_list var_defaults = adf.variable_defaults + comp = adf.model_component + if comp == "atm": + spec_vars = ["PMID", "OCNFRAC", "LANDFRAC"] + mask_var = "OCNFRAC" + mask_name = "ocean" + if comp == "lnd": + spec_vars = ["LANDFRAC"] + mask_var = "LANDFRAC" + mask_name = "land" + #CAM simulation variables (these quantities are always lists): case_names = adf.get_cam_info("cam_case_name", required=True) - input_climo_locs = adf.get_cam_info("cam_climo_loc", required=True) + output_climo_locs = adf.get_cam_info("cam_climo_loc", required=True) + + # SE to FV options + case_latlon_files = adf.latlon_files["test_latlon_file"] + case_wgts_files = adf.latlon_wgt_files["test_wgts_file"] + case_methods = adf.latlon_regrid_method["test_regrid_method"] #Grab case years syear_cases = adf.climo_yrs["syears"] @@ -66,7 +84,7 @@ def regrid_and_vert_interp(adf): #Check if mid-level pressure, ocean fraction or land fraction exist #in the variable list: - for var in ["PMID", "OCNFRAC", "LANDFRAC"]: + for var in spec_vars: if var in var_list: #If so, then move them to the front of variable list so #that they can be used to mask or vertically interpolate @@ -79,8 +97,8 @@ def regrid_and_vert_interp(adf): #Create new variables that potentially stores the re-gridded #ocean/land fraction dataset: - ocn_frc_ds = None - tgt_ocn_frc_ds = None + frc_ds = None + tgt_frc_ds = None #Check if surface pressure exists in variable list: if "PS" in var_list: @@ -128,15 +146,15 @@ def regrid_and_vert_interp(adf): #Set output/target data path variables: #------------------------------------ rgclimo_loc = Path(output_loc) - if not adf.compare_obs: - tclimo_loc = Path(target_loc) - #------------------------------------ - #Check if re-gridded directory exists, and if not, then create it: if not rgclimo_loc.is_dir(): print(f" {rgclimo_loc} not found, making new directory") rgclimo_loc.mkdir(parents=True) #End if + if not adf.compare_obs: + tclimo_loc = Path(target_loc) + #------------------------------------ + #Loop over CAM cases: for case_idx, case_name in enumerate(case_names): @@ -144,11 +162,8 @@ def regrid_and_vert_interp(adf): #Notify user of model case being processed: print(f"\t Regridding case '{case_name}' :") - #Set case climo data path: - mclimo_loc = Path(input_climo_locs[case_idx]) - #Create empty dictionaries which store the locations of regridded surface - #pressure and mid-level pressure fields: + #pressure and mid-level pressure fields if needed: ps_loc_dict = {} pmid_loc_dict = {} @@ -187,12 +202,13 @@ def regrid_and_vert_interp(adf): #Determine regridded variable file name: regridded_file_loc = rgclimo_loc / f'{target}_{case_name}_{var}_regridded.nc' - #If surface or mid-level pressure, then save for potential use by other variables: - if var == "PS": - ps_loc_dict[target] = regridded_file_loc - elif var == "PMID": - pmid_loc_dict[target] = regridded_file_loc - #End if + if comp == "atm": + #If surface or mid-level pressure, then save for potential use by other variables: + if var == "PS": + ps_loc_dict[target] = regridded_file_loc + elif var == "PMID": + pmid_loc_dict[target] = regridded_file_loc + #End if #Check if re-gridded file already exists and over-writing is allowed: if regridded_file_loc.is_file() and overwrite_regrid: @@ -209,16 +225,14 @@ def regrid_and_vert_interp(adf): #For now, only grab one file (but convert to list for use below): tclim_fils = [tclimo_loc] else: - tclim_fils = sorted(tclimo_loc.glob(f"{target}*_{var}_climo.nc")) + tclim_fils = adf.data.get_reference_climo_file(var) #End if #Write to debug log if enabled: - adf.debug_log(f"regrid_example: tclim_fils (n={len(tclim_fils)}): {tclim_fils}") - - if len(tclim_fils) > 1: - #Combine all target files together into a single data set: - tclim_ds = xr.open_mfdataset(tclim_fils, combine='by_coords') - elif len(tclim_fils) == 0: + #adf.debug_log(f"regrid_example: tclim_fils (n={len(tclim_fils)}): {tclim_fils}") + print(var) + tclim_ds = adf.data.load_reference_climo_dataset(var) + if tclim_ds is None: print(f"\t WARNING: regridding {var} failed, no climo file for case '{target}'. Continuing to next variable.") continue else: @@ -226,75 +240,105 @@ def regrid_and_vert_interp(adf): tclim_ds = xr.open_dataset(tclim_fils[0]) #End if - #Generate CAM climatology (climo) file list: - mclim_fils = sorted(mclimo_loc.glob(f"{case_name}_{var}_*.nc")) - - if len(mclim_fils) > 1: - #Combine all cam files together into a single data set: - mclim_ds = xr.open_mfdataset(mclim_fils, combine='by_coords') - elif len(mclim_fils) == 0: - #wmsg = f"\t WARNING: Unable to find climo file for '{var}'." - #wmsg += " Continuing to next variable." - wmsg= f"\t WARNING: regridding {var} failed, no climo file for case '{case_name}'. Continuing to next variable." - print(wmsg) + mclim_ds = adf.data.load_climo_dataset(case_name, var) + if mclim_ds is None: + print(f"\t WARNING: regridding {var} failed, no climo file for case '{target}'. Continuing to next variable.") continue - else: - #Open single file as new xarray dataset: - mclim_ds = xr.open_dataset(mclim_fils[0]) - #End if #Create keyword arguments dictionary for regridding function: regrid_kwargs = {} - #Check if target in relevant pressure variable dictionaries: - if target in ps_loc_dict: - regrid_kwargs.update({'ps_file': ps_loc_dict[target]}) - #End if - if target in pmid_loc_dict: - regrid_kwargs.update({'pmid_file': pmid_loc_dict[target]}) - #End if + if comp == "atm": + #Check if target in relevant pressure variable dictionaries: + if target in ps_loc_dict: + regrid_kwargs.update({'ps_file': ps_loc_dict[target]}) + #End if + if target in pmid_loc_dict: + regrid_kwargs.update({'pmid_file': pmid_loc_dict[target]}) + #End if + + if ('lat' not in mclim_ds.dims) and ('lat' not in mclim_ds.dims): + if ('ncol' in mclim_ds.dims) or ('lndgrid' in mclim_ds.dims): + print(f"\t INFO: Looks like test case '{case_name}' is unstructured, eh?") + + #Check if any a FV file exists if using native grid + case_latlon_file = case_latlon_files[case_idx] + if not case_latlon_file: + msg = "WARNING: This looks like an unstructured case, but missing lat/lon file" + print(msg) + case_latlon_file = None + #raise AdfError(msg) + + #Check if any a weights file exists if using native grid + case_wgts_file = case_wgts_files[case_idx] + if not case_wgts_file: + msg = "WARNING: This looks like an unstructured case, but missing weights file, can't continue." + raise AdfError(msg) + + case_method = case_methods[case_idx] + + # Grid unstructured climo if applicable before regridding + rgdata_interp = _regrid(mclim_ds, var, + comp=comp, + wgt_file=case_wgts_file, + latlon_file=case_latlon_file, + method=case_method, + ) + + output_test_loc = Path(output_climo_locs[case_idx]) + rgridded_output_loc = output_test_loc / "gridded" + if not rgridded_output_loc.is_dir(): + print(f" {rgridded_output_loc} not found, making new directory") + rgridded_output_loc.mkdir(parents=True) + save_to_nc(rgdata_interp, rgridded_output_loc / f'{case_name}_{var}_gridded_climo.nc') - #Perform regridding and interpolation of variable: - rgdata_interp = _regrid_and_interpolate_levs(mclim_ds, var, + else: + msg = "WARNING: No lat/lons but no grid info either. I guess this really is a problem!" + msg += "\n You might want to look at the files. Only CAM and CLM (ncol) and CLM (lndgrd) native grids are acceptable." + raise AdfError(msg) + else: + rgdata_interp = mclim_ds + #else: + rgdata_interp = _regrid_and_interpolate_levs(rgdata_interp, var, regrid_dataset=tclim_ds, **regrid_kwargs) - #Extract defaults for variable: var_default_dict = var_defaults.get(var, {}) if 'mask' in var_default_dict: - if var_default_dict['mask'].lower() == 'ocean': + if var_default_dict['mask'].lower() == mask_name: #Check if the ocean fraction has already been regridded #and saved: - if ocn_frc_ds: - ofrac = ocn_frc_ds['OCNFRAC'] + if frc_ds: + frac = frc_ds[mask_var] # set the bounds of regridded ocnfrac to 0 to 1 - ofrac = xr.where(ofrac>1,1,ofrac) - ofrac = xr.where(ofrac<0,0,ofrac) + frac = xr.where(frac>1,1,frac) + frac = xr.where(frac<0,0,frac) # apply ocean fraction mask to variable - rgdata_interp['OCNFRAC'] = ofrac + rgdata_interp[mask_var] = frac var_tmp = rgdata_interp[var] - var_tmp = pf.mask_land_or_ocean(var_tmp,ofrac) + var_tmp = pf.mask_land_or_ocean(var_tmp,frac) rgdata_interp[var] = var_tmp else: - print(f"\t WARNING: OCNFRAC not found, unable to apply mask to '{var}'") + print(f"\t WARNING: {mask_var} not found, unable to apply mask to '{var}'") #End if else: - #Currently only an ocean mask is supported, so print warning here: - wmsg = "\t WARNING: Currently the only variable mask option is 'ocean'," + #Currently only ocean or land masks are supported, so print warning here: + wmsg = f"\t WARNING: Currently the only variable mask option is '{mask_name}'," wmsg += f"not '{var_default_dict['mask'].lower()}'" print(wmsg) #End if #End if - #If the variable is ocean fraction, then save the dataset for use later: - if var == 'OCNFRAC': - ocn_frc_ds = rgdata_interp + #If the variable is the mask fraction, then save the dataset for use later: + if var == mask_var: + frc_ds = rgdata_interp #End if #Finally, write re-gridded data to output file: #Convert the list of Path objects to a list of strings + mclim_fils = adf.data.get_climo_file(case_name, var) climatology_files_str = [str(path) for path in mclim_fils] climatology_files_str = ', '.join(climatology_files_str) test_attrs_dict = { @@ -310,31 +354,71 @@ def regrid_and_vert_interp(adf): #if applicable: #Set interpolated baseline file name: + #interp_bl_file = trgclimo_loc / f'{target}_{var}_baseline.nc' interp_bl_file = rgclimo_loc / f'{target}_{var}_baseline.nc' if not adf.compare_obs and not interp_bl_file.is_file(): + if comp == "atm": + #Look for a baseline climo file for surface pressure (PS): + bl_ps_fil = tclimo_loc / f'{target}_PS_climo.nc' - #Look for a baseline climo file for surface pressure (PS): - bl_ps_fil = tclimo_loc / f'{target}_PS_climo.nc' + #Also look for a baseline climo file for mid-level pressure (PMID): + bl_pmid_fil = tclimo_loc / f'{target}_PMID_climo.nc' - #Also look for a baseline climo file for mid-level pressure (PMID): - bl_pmid_fil = tclimo_loc / f'{target}_PMID_climo.nc' + #Create new keyword arguments dictionary for regridding function: + regrid_kwargs = {} - #Create new keyword arguments dictionary for regridding function: - regrid_kwargs = {} - - #Check if PS and PMID files exist: - if bl_ps_fil.is_file(): - regrid_kwargs.update({'ps_file': bl_ps_fil}) - #End if - if bl_pmid_fil.is_file(): - regrid_kwargs.update({'pmid_file': bl_pmid_fil}) - #End if - - #Generate vertically-interpolated baseline dataset: - tgdata_interp = _regrid_and_interpolate_levs(tclim_ds, var, - **regrid_kwargs) + #Check if PS and PMID files exist: + if bl_ps_fil.is_file(): + regrid_kwargs.update({'ps_file': bl_ps_fil}) + #End if + if bl_pmid_fil.is_file(): + regrid_kwargs.update({'pmid_file': bl_pmid_fil}) + #End if + + #if unstruct_base: + if ('lat' not in tclim_ds.dims) and ('lat' not in tclim_ds.dims): + if ('ncol' in tclim_ds.dims) or ('lndgrid' in tclim_ds.dims): + print(f"\t INFO: Looks like baseline case '{target}' is unstructured, eh?") + + #Check if any a FV file exists if using native grid + baseline_latlon_file = adf.latlon_files["baseline_latlon_file"] + if not baseline_latlon_file: + msg = "WARNING: This looks like an unstructured case, but missing lat/lon file" + print(msg) + baseline_latlon_file = None + #raise AdfError(msg) + + #Check if any a weights file exists if using native grid + baseline_wgts_file = adf.latlon_wgt_files["baseline_wgts_file"] + if not baseline_wgts_file: + msg = "WARNING: This looks like an unstructured case, but missing weights file, can't continue." + raise AdfError(msg) + + base_method = adf.latlon_regrid_method["baseline_regrid_method"] + + # Grid unstructured climo if applicable before regridding + tgdata_interp = _regrid(tclim_ds, var, + comp=comp, + wgt_file=baseline_wgts_file, + latlon_file=baseline_latlon_file, + method=base_method, + ) + tgridded_output_loc = Path(target_loc) / "gridded" + if not tgridded_output_loc.is_dir(): + print(f" {tgridded_output_loc} not found, making new directory") + tgridded_output_loc.mkdir(parents=True) + save_to_nc(tgdata_interp, tgridded_output_loc / f'{target}_{var}_gridded_climo.nc') + else: + msg = "WARNING: No lat/lons but no grid info either. I guess this really is a problem!" + msg += "\n You might want to look at the files. Only CAM (ncol) and CLM (lndgrd) native grids are acceptable." + raise AdfError(msg) + else: + tgdata_interp = tclim_ds + tgdata_interp = _regrid_and_interpolate_levs(tgdata_interp, var, + regrid_dataset=tclim_ds, + **regrid_kwargs) if tgdata_interp is None: #Something went wrong during interpolation, so just cycle through #for now: @@ -342,29 +426,32 @@ def regrid_and_vert_interp(adf): #End if #If the variable is ocean fraction, then save the dataset for use later: - if var == 'OCNFRAC': - tgt_ocn_frc_ds = tgdata_interp + if var == mask_var: + frc_ds = tgdata_interp #End if - if 'mask' in var_default_dict: - if var_default_dict['mask'].lower() == 'ocean': + if var_default_dict['mask'].lower() == mask_name: #Check if the ocean fraction has already been regridded #and saved: - if tgt_ocn_frc_ds: - ofrac = tgt_ocn_frc_ds['OCNFRAC'] + if frc_ds: + frac = frc_ds[mask_var] # set the bounds of regridded ocnfrac to 0 to 1 - ofrac = xr.where(ofrac>1,1,ofrac) - ofrac = xr.where(ofrac<0,0,ofrac) - # mask the land in TS for global means - tgdata_interp['OCNFRAC'] = ofrac - ts_tmp = tgdata_interp[var] - ts_tmp = pf.mask_land_or_ocean(ts_tmp,ofrac) - tgdata_interp[var] = ts_tmp + frac = xr.where(frac>1,1,frac) + frac = xr.where(frac<0,0,frac) + + # apply ocean fraction mask to variable + rgdata_interp[mask_var] = frac + var_tmp = rgdata_interp[var] + var_tmp = pf.mask_land_or_ocean(var_tmp,frac) + rgdata_interp[var] = var_tmp else: - wmsg = "\t WARNING: OCNFRAC not found in target," - wmsg += f" unable to apply mask to '{var}'" - print(wmsg) + print(f"\t WARNING: {mask_var} not found, unable to apply mask to '{var}'") #End if + else: + #Currently only ocean or land masks are supported, so print warning here: + wmsg = f"\t WARNING: Currently the only variable mask option is '{mask_name}'," + wmsg += f"not '{var_default_dict['mask'].lower()}'" + print(wmsg) #End if #End if @@ -494,7 +581,6 @@ def _regrid_and_interpolate_levs(model_dataset, var_name, regrid_dataset=None, r #Check if variable has a vertical levels dimension: if has_lev: - if vert_coord_type == "hybrid": # Need hyam, hybm, and P0 for vertical interpolation of hybrid levels: if 'lev' in mdata.dims: @@ -616,7 +702,6 @@ def _regrid_and_interpolate_levs(model_dataset, var_name, regrid_dataset=None, r #Interpolate variable to default pressure levels: if has_lev: - if vert_coord_type == "hybrid": #Interpolate from hybrid sigma-pressure to the standard pressure levels: rgdata_interp = pf.lev_to_plev(rgdata, rg_ps, mhya, mhyb, P0=P0, \ @@ -709,4 +794,143 @@ def regrid_data(fromthis, tothis, method=1): return result #End if -##### \ No newline at end of file +##### + + +import numpy as np + +def _regrid(model_dataset, var_name, comp, wgt_file, method, latlon_file, **kwargs): + + """ + Function that takes a variable from a model xarray + dataset, regrids it to another dataset's lat/lon + coordinates (if applicable) + ---------- + model_dataset -> The xarray dataset which contains the model variable data + var_name -> The name of the variable to be regridded/interpolated. + comp -> + wgt_file -> + method -> + latlon_file -> + + Optional inputs: + + kwargs -> Keyword arguments that contain paths to THE REST IS NOT APPLICABLE: surface pressure + and mid-level pressure files, which are necessary for + certain types of vertical interpolation. + This function returns a new xarray dataset that contains the gridded + model variable. + """ + + #Import ADF-specific functions: + from regrid_se_to_fv import make_se_regridder, regrid_se_data_conservative, regrid_se_data_bilinear, regrid_atm_se_data_conservative, regrid_atm_se_data_bilinear + + if comp == "atm": + comp_grid = "ncol" + if comp == "lnd": + comp_grid = "lndgrid" + if latlon_file: + latlon_ds = xr.open_dataset(latlon_file) + else: + print("Looks like no lat lon file is supplied. God speed!") + + model_dataset[var_name] = model_dataset[var_name].fillna(0) + + if comp == "lnd": + model_dataset['landfrac'] = model_dataset['landfrac'].fillna(0) + #mdata = mdata * model_dataset.landfrac # weight flux by land frac + model_dataset[var_name] = model_dataset[var_name] * model_dataset.landfrac # weight flux by land frac + s_data = model_dataset.landmask.isel(time=0) + d_data = latlon_ds.landmask + else: + s_data = None + d_data = None + + #Grid model data to match target grid lat/lon: + regridder = make_se_regridder(weight_file=wgt_file, + s_data = s_data, + d_data = d_data, + Method = method, + ) + + if comp == "lnd": + if method == 'coservative': + rgdata = regrid_se_data_conservative(regridder, model_dataset, comp_grid) + if method == 'bilinear': + rgdata = regrid_se_data_bilinear(regridder, model_dataset, comp_grid) + rgdata[var_name] = (rgdata[var_name] / rgdata.landfrac) + + if comp == "atm": + if method == 'coservative': + rgdata = regrid_atm_se_data_conservative(regridder, model_dataset, comp_grid) + if method == 'bilinear': + rgdata = regrid_atm_se_data_bilinear(regridder, model_dataset, comp_grid) + + + #rgdata['lat'] = latlon_ds.lat #??? + if comp == "lnd": + rgdata['landmask'] = latlon_ds.landmask + rgdata['landfrac'] = rgdata.landfrac.isel(time=0) + + # calculate area + rgdata = _calc_area(rgdata) + + #Return dataset: + return rgdata + + +def _calc_area(rgdata): + # calculate area + area_km2 = np.zeros(shape=(len(rgdata['lat']), len(rgdata['lon']))) + earth_radius_km = 6.37122e3 # in meters + + yres_degN = np.abs(np.diff(rgdata['lat'].data)) # distances between gridcell centers... + xres_degE = np.abs(np.diff(rgdata['lon'])) # ...end up with one less element, so... + yres_degN = np.append(yres_degN, yres_degN[-1]) # shift left (edges <-- centers); assume... + xres_degE = np.append(xres_degE, xres_degE[-1]) # ...last 2 distances bet. edges are equal + + dy_km = yres_degN * earth_radius_km * np.pi / 180 # distance in m + phi_rad = rgdata['lat'].data * np.pi / 180 # degrees to radians + + # grid cell area + for j in range(len(rgdata['lat'])): + for i in range(len(rgdata['lon'])): + dx_km = xres_degE[i] * np.cos(phi_rad[j]) * earth_radius_km * np.pi / 180 # distance in m + area_km2[j,i] = dy_km[j] * dx_km + + rgdata['area'] = xr.DataArray(area_km2, + coords={'lat': rgdata.lat, 'lon': rgdata.lon}, + dims=["lat", "lon"]) + rgdata['area'].attrs['units'] = 'km2' + rgdata['area'].attrs['long_name'] = 'Grid cell area' + + return rgdata + + +def _calculate_area(rgdata): + """ + Compute grid cell area for regridded dataset. + """ + area_km2 = np.zeros((len(rgdata['lat']), len(rgdata['lon']))) + earth_radius_km = 6.37122e3 + + yres_degN = np.abs(np.diff(rgdata['lat'].data)) + xres_degE = np.abs(np.diff(rgdata['lon'])) + + yres_degN = np.append(yres_degN, yres_degN[-1]) + xres_degE = np.append(xres_degE, xres_degE[-1]) + + dy_km = yres_degN * earth_radius_km * np.pi / 180 + phi_rad = rgdata['lat'].data * np.pi / 180 + + for j in range(len(rgdata['lat'])): + for i in range(len(rgdata['lon'])): + dx_km = xres_degE[i] * np.cos(phi_rad[j]) * earth_radius_km * np.pi / 180 + area_km2[j, i] = dy_km[j] * dx_km + + return xr.DataArray(area_km2, coords={'lat': rgdata.lat, 'lon': rgdata.lon}, dims=["lat", "lon"], attrs={'units': 'km2', 'long_name': 'Grid cell area'}) + + + + + diff --git a/scripts/regridding/regrid_conservative.ipynb b/scripts/regridding/regrid_conservative.ipynb new file mode 100644 index 000000000..59f69bf2b --- /dev/null +++ b/scripts/regridding/regrid_conservative.ipynb @@ -0,0 +1,4200 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3c9c6614-73bd-48e7-aabb-b293041f93e1", + "metadata": {}, + "source": [ + "#### Created weight file The first one (from mesh files) didn't work\n", + "\n", + "```\n", + "ESMF_RegridWeightGen --source /glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc --destination /glade/campaign/cesm/cesmdata/inputdata/share/meshes/fv0.9x1.25_141008_polemod_ESMFmesh.nc --weight /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_nomask_c250108.nc --method conserve\n", + "```\n", + "\n", + "#### This sencond one (from scripgrid files) has the right dimensions for dst_grid_dims (192x288) \n", + "TODO:\n", + "- what's the correct method here?\n", + "- appropriate to use scripgrids\n", + "- provide these for more common resolutions?\n", + "```\n", + "ESMF_RegridWeightGen --source /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/ne30pg3_scrip_170611.nc --destination /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/fv0.9x1.25_141008.nc --weight /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_nomask_c250108.nc --method conserve2nd --ignore_unmapped --ignore_degenerate --pole none\n", + "```\n", + "\n", + "Trying to get the pole in lat (as in the FV09 grid), didn't work. \n", + "adding pole all required a method other that conserve2nd\n", + "**Currently using this**\n", + "```\n", + "ESMF_RegridWeightGen --source /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/ne30pg3_scrip_170611.nc --destination /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/fv0.9x1.25_141008.nc --weight /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc --method conserve\n", + "```\n", + "\n", + "```\n", + "ESMF_RegridWeightGen --source /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/ne30pg3_scrip_170611.nc --destination /glade/campaign/cesm/cesmdata/inputdata/share/scripgrids/fv0.9x1.25_141008.nc --weight /glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_nomask_c250108.nc --method bilinear --pole all --ignore_unmapped\n", + "```\n", + "\n", + "\n", + "#### Also added area and land frac to single variable time series\n", + "```\n", + "ncks -A -v area,landfrac,landmask /glade/derecho/scratch/hannay/archive/b.e30_beta04.BLT1850.ne30_t232_wgx3.121/lnd/hist/b.e30_beta04.BLT1850.ne30_t232_wgx3.121.clm2.h0.0012-10.nc /glade/derecho/scratch/wwieder/ADF/b.e30_beta04.BLT1850.ne30_t232_wgx3.121/climo/b.e30_beta04.BLT1850.ne30_t232_wgx3.121_GPP_climo.nc\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "09508471-56bc-4cc5-8011-49456d42afea", + "metadata": {}, + "outputs": [], + "source": [ + "import os, sys\n", + "import shutil\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "import numpy as np\n", + "import xarray as xr\n", + "import xesmf as xe\n", + "import regrid_se_to_fv\n", + "\n", + "# Helpful for plotting only\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import cartopy\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import uxarray as ux #need npl 2024a or later\n", + "import geoviews.feature as gf" + ] + }, + { + "cell_type": "markdown", + "id": "1ea71ab6-09b4-4a2f-8979-a1532b5df098", + "metadata": {}, + "source": [ + "#### Conservative regridding\n", + "- set missing values to zero\n", + "- Weight fluxes by source landfrac, \n", + "- Regrid, then\n", + "- Divide by regridded landfrac\n", + "- Calculate global and regional sums\n", + "- For plotting add destination landmask to get rid of bloated coastlines\n", + "\n", + "#### At the end of the day we want to write out a destination grid .nc file with:\n", + "- regridded field\n", + "- regridded land frac\n", + "- wall to wall area (currently from CAM history file)\n", + "- destination grid land mask\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bac26d5c-492e-4b35-9476-b6601de4bb06", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Dummy source file to regrid (for now this can be from climo files made by adf)\n", + "gppfile='/glade/derecho/scratch/wwieder/ADF/b.e30_beta04.BLT1850.ne30_t232_wgx3.121/climo/b.e30_beta04.BLT1850.ne30_t232_wgx3.121_GPP_climo.nc'\n", + "ds_con = xr.open_dataset(gppfile)\n", + "\n", + "# Weighting file needed for regridding, keep this hard coded for now.\n", + "con_weight_file = \"/glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_conserve_nomask_c250108.nc\"\n", + "\n", + "# dummy destination grid\n", + "fv_t232_file = '/glade/derecho/scratch/wwieder/ctsm5.3.018_SP_f09_t232_mask/run/ctsm5.3.018_SP_f09_t232_mask.clm2.h0.0001-01.nc'\n", + "fv_t232 = xr.open_dataset(fv_t232_file)\n", + "\n", + "# Fill in missing values with zeros\n", + "ds_con['GPP'] = ds_con['GPP'].fillna(0) \n", + "ds_con['landfrac']= ds_con['landfrac'].fillna(0) \n", + "ds_con['GPP'] = ds_con.GPP * ds_con.landfrac # weight flux by land frac\n", + "\n", + "# not used for actually regridding\n", + "#ds_con['test'] = ((ds_con.GPP)*0+1.)\n", + "#ds_con['test'] = ds_con.test * ds_con.landfrac\n", + "\n", + "# These are the calls to regrid the souce data\n", + "regridder = regrid_se_to_fv.make_se_regridder(weight_file=con_weight_file, \n", + " s_data = ds_con.landmask.isel(time=0), \n", + " d_data = fv_t232.landmask,\n", + " Method = 'coservative',\n", + " )\n", + "ds_out_con = regrid_se_to_fv.regrid_se_data_conservative(regridder, ds_con).load()\n", + "\n", + "# Post processing to finish the conversion correctly:\n", + "ds_out_con['GPP'] = (ds_out_con.GPP / ds_out_con.landfrac)\n", + "#ds_out_con['test'] = (ds_out_con.test / ds_out_con.landfrac)\n", + "\n", + "# drop time variables\n", + "ds_out_con['landfrac'] = ds_out_con['landfrac'].isel(time=0) \n", + "ds_out_con['area'] = ds_out_con['area'].isel(time=0) \n", + "ds_out_con['landmask'] = ds_out_con['landmask'].isel(time=0) \n", + "\n", + "# TODO, add a global area and landmask field from the destination grid for calculating sums and plotting.\n", + "# TODO save this as a .nc file\n", + "# TODO, drop the test field from this once integrated into ADF\n", + "# Quick check of results\n", + "ds_out_con.GPP.isel(time=0).plot() ;" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7f58c441-3f24-4791-8616-e69cbe28ba43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray 'GPP' (time: 12, lndgrid: 48600)> Size: 2MB\n",
+       "array([[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 9.2530281e-06,\n",
+       "        4.7339649e-06, 1.7577652e-06],\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 8.1039307e-06,\n",
+       "        4.2056104e-06, 1.5534362e-06],\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.0241940e-05,\n",
+       "        5.4556108e-06, 2.0470754e-06],\n",
+       "       ...,\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 3.4669843e-05,\n",
+       "        1.6131389e-05, 4.9520345e-06],\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 2.7128302e-05,\n",
+       "        1.3086953e-05, 3.9950073e-06],\n",
+       "       [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.4469586e-05,\n",
+       "        7.3627734e-06, 2.6298280e-06]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time     (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "Dimensions without coordinates: lndgrid
    " + ], + "text/plain": [ + " Size: 2MB\n", + "array([[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 9.2530281e-06,\n", + " 4.7339649e-06, 1.7577652e-06],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 8.1039307e-06,\n", + " 4.2056104e-06, 1.5534362e-06],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.0241940e-05,\n", + " 5.4556108e-06, 2.0470754e-06],\n", + " ...,\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 3.4669843e-05,\n", + " 1.6131389e-05, 4.9520345e-06],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 2.7128302e-05,\n", + " 1.3086953e-05, 3.9950073e-06],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.4469586e-05,\n", + " 7.3627734e-06, 2.6298280e-06]], dtype=float32)\n", + "Coordinates:\n", + " * time (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n", + "Dimensions without coordinates: lndgrid" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_con.GPP" + ] + }, + { + "cell_type": "markdown", + "id": "e42505aa-4d41-42d3-8311-497209386c38", + "metadata": {}, + "source": [ + "#### Bilinear regridding\n", + "- Include a mask\n", + "- set `skipna=True, na_thres=1` in xEMSF regridder\n", + "- Weighting fluxes landfrac degrades results\n", + "- destination Mask where destination landfrac > 0 to avoid bloated coastlines" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "72f6f0b2-bb21-47eb-9205-934aadde8d57", + "metadata": {}, + "outputs": [], + "source": [ + "bilin_weight_file = \"/glade/work/wwieder/map_ne30pg3_to_fv0.9x1.25_scripgrids_bilinear_nomask_c250108.nc\"\n", + "ds_bilin = xr.open_dataset(gppfile)\n", + "ds_bilin['test'] = ((ds_bilin.GPP)*0+1.)\n", + "ds_bilin['mask'] = ds_bilin.landmask \n", + "\n", + "# Read in weight file and regrid\n", + "regridder = regrid_se_to_fv.make_se_regridder(weight_file=bilin_weight_file, \n", + " s_data = ds_con.landmask.isel(time=0), \n", + " d_data = fv_t232.landmask,\n", + " Method='bilinear',\n", + " )\n", + "ds_out_bilin = regrid_se_to_fv.regrid_se_data_bilinear(regridder, ds_bilin).load()\n", + "ds_out_bilin['landfrac'] = ds_out_bilin['landfrac'].isel(time=0) \n", + "ds_out_bilin['area'] = ds_out_bilin['area'].isel(time=0) \n", + "ds_out_bilin['landmask'] = ds_out_bilin['landmask'].isel(time=0) " + ] + }, + { + "cell_type": "markdown", + "id": "3d49c3a6-db67-4795-9ceb-ad7e7a8cea1d", + "metadata": {}, + "source": [ + "----\n", + "#### Quick look at g17 vs. t232 masks and regridded results" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7186b5ff-76df-4af3-a974-fd0f4840af9c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mesh0 = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'\n", + "ds0 = ux.open_dataset(mesh0, gppfile)\n", + "ds0['test'] = (ds0.GPP)*0+1\n", + "\n", + "mesh1 = '/glade/campaign/cesm/cesmdata/inputdata/share/meshes/fv0.9x1.25_141008_ESMFmesh.nc'\n", + "fv_g17_file = '/glade/derecho/scratch/oleson/ANALYSIS/climo/ctsm53n04ctsm52028_f09_hist/ctsm53n04ctsm52028_f09_hist_annT_1850.nc'\n", + "fv_g17 = xr.open_dataset(fv_g17_file)\n", + "ux_g17 = ux.open_dataset(mesh1, fv_g17_file)\n", + "\n", + "#CLM output already has area masked\n", + "fv_cam_file = '/glade/campaign/cgd/cesm/CESM2-LE/atm/proc/tseries/month_1/AREA/b.e21.BSSP370smbb.f09_g17.LE2-1301.020.cam.h0.AREA.209501-210012.nc'\n", + "fv_cam_area = xr.open_dataset(fv_cam_file)['AREA'].isel(time=0)*1e-6 # convert m2 to km2\n", + "fv_cam_area.attrs['units'] = fv_t232['area'].attrs['units']\n", + "\n", + "# Plot the two masks\n", + "fig, axs = plt.subplots(nrows=2,ncols=1,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(8,7))\n", + "\n", + "# axs is a 2 dimensional array of `GeoAxes`. We will flatten it into a 1-D array\n", + "axs=axs.flatten()\n", + "\n", + "fv_g17.GPP.isel(time=0).plot(ax=axs[0])\n", + "axs[0].set_title('f09-g17 mask')\n", + "\n", + "fv_t232.FPSN.isel(time=0).plot(ax=axs[1])\n", + "axs[1].set_title('f09-t232 mask') ;\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "511aac49-6baa-477c-aa17-4f7c7ad9d617", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# calculate are directly from weight file.\n", + "ds_weight = xr.open_dataset(con_weight_file)\n", + "\n", + "SHR_CONST_REARTH = 6.37122e3 # radius of earth ~ km\n", + "area_raw = ds_weight.area_b * (SHR_CONST_REARTH**2)\n", + "area_shape = xr.DataArray(area_raw.data.reshape(192,288), dims=(\"lat\", \"lon\"))\n", + "fig, axs = plt.subplots(nrows=1,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(16,4))\n", + "axs=axs.flatten()\n", + "(fv_cam_area).plot(ax=axs[0]) ;\n", + "# fv_t232.area.plot(ax=axs[1]) ;\n", + "area_shape.plot(ax=axs[1], x='lon',y='lat');" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3eb7bc4d-8c25-4273-a739-1c2ff0f9778a", + "metadata": {}, + "outputs": [], + "source": [ + "# add wall to wall area to clm history file\n", + "fv_cam_area['lat'] = fv_t232.lat\n", + "fv_cam_area['lon'] = fv_t232.lon\n", + "fv_t232['area'] = fv_cam_area" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2527cd61-ebea-4762-a7b3-4cd5e8782285", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# lats are not the same on destination grid, adjusting now\n", + "ds_out_con['lat'] = fv_t232.lat\n", + "ds_out_bilin['lat'] = fv_t232.lat\n", + "\n", + "# Plot the two masks\n", + "fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(16,8))\n", + "\n", + "axs=axs.flatten()\n", + "ds_out_con.GPP.isel(time=0).plot(ax=axs[0],vmin=0,vmax=1e-4)\n", + "axs[0].set_title('Cons. raw')\n", + "\n", + "ds_out_con.GPP.isel(time=0).where(ds_out_con.landfrac > 0).plot(ax=axs[1],vmin=0,vmax=1e-4)\n", + "axs[1].set_title('Cons. regridded mask')\n", + "\n", + "ds_out_bilin.GPP.isel(time=0).plot(ax=axs[2],vmin=0,vmax=1e-4)\n", + "axs[2].set_title('Bilin. raw')\n", + "\n", + "ds_out_bilin.GPP.isel(time=0).where(fv_t232.landfrac>0).plot(ax=axs[3],vmin=0,vmax=1e-4)\n", + "axs[3].set_title('Bilin. dest mask') ;\n", + "\n", + "## Go ahead and apply the mask based on destination grid?\n", + "# Currently conservative only has mask based on remapped landfrac\n", + "# Bilinear with destination landfrac mask\n", + "ds_out_con = ds_out_con.where(ds_out_con.landfrac>0)\n", + "ds_out_bilin = ds_out_bilin.where(fv_t232.landfrac>0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9231d764-7083-4af8-a10f-6edbf81a7271", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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cOBGoGTu7cuVK1q9fzz/+8Q+AOu+huwoKClBK1ft5RUdHA5CXl1dn++9/1BxvZ2PnP15HRETECfvq2wb1/xv6I08+YyGEaC0FBQU4nc5GZyjOy8tr1u9fd2KUu/HuuIa+i2+88UYqKyv54osvAJg/fz4ZGRm1ySdAWloao0eP5tixY7z88sssX76c9evX144ZbUrcAggODmbKlCl89NFHteNZZ86cyZAhQ+jduzdQ8945HA5effXVE67zvPPOAzjhWuvzx+tvSr3BwcF1/jaZTCfdfjz2e/r+1RdPj8fCP/5baihuVldXU1pa6tF15uXltVgcbuzf/PHrauj/I5fLRUFBQaPnaenPyJPfj+vWrWPfvn1ceeWVMsu5AGS25w7hj+urFhUVMXfuXB555BH+9re/1W6vqqoiPz+/TlmTycSoUaNYuHAhsbGxREZGkpycTKdOnYCayQkWLVp0wl26PwoKCkLTNDIzM0/Y98dtoaGhaJrG8uXLa79Yf+/325KTk/niiy9QSrFt2zZmzpzJY489htVqrXNtze2LL77AaDQyd+7cOolyfesJuysoKAidTkdGRsYJ+45PYhUaGtrk+o87HryysrJO2Fff5wMn/huqjyefsRBCtJbg4GD0ev1JJ7KBmu/G5v7+bSxGeRLvoOHv4l69ejFkyBA++OADbrvtNj744AOio6Nrb9JCTXwqKytj9uzZJCQk1G7fsmWLx9f1R9OnT2fWrFksWLCA+Ph41q9fz5tvvlm7PygoqLYX05/+9Kd66zjZDfTj/nj9zVWvOzx9/04WY/+YRDYUN00mEzabDaPR6PZ1hoSEtFkcPn5dDf1/pNPp6jy5b26efEbu/n688soriYyM5B//+Acul4t//vOfLdZ+0THIk98OSNM0lFInBNV33323ziyEx40fP56NGzfyzTff1HZt9vX1ZdiwYbz66qukp6eftMvz8fJDhgxh9uzZdZ6glpSU8MMPP9Qpe8EFF6CU4tixYwwePPiEV3Jycr3X1K9fP1588UUCAwPZtGlT7T6z2dzkO9oN0TQNg8FQp/tPRUUFH3/88Qll3T2/r68vQ4cOZfbs2XXKu1wuPvnkE2JjY+nWrdspt7179+5ERkby1Vdf1dmelpZW2729KTz5jIUQorVYrVbOOussZs2addKni+PGjWPx4sUnzJj/0Ucf4ePjc0pL/DQUo5oS7xoyffp01q5dy4oVK/jhhx+44YYb6sSo44nj72O/UqreWWs9jZsTJ04kJiaGDz74gA8++ACLxcLVV19du9/Hx4cxY8awefNm+vbtW++1/jEhdEdL1VsfT94/qIl9v5/pGmrWT9bpdJx55pl1tjcUN0ePHo1er/foOseMGcPOnTvZunXrCeduad27dycmJobPPvuszsoXZWVlfPPNN7UzQLcUTz+j48c09PvxuH/+85+89NJLPPzwwzz00EPN33DRociT3w7I39+fM888k+eee47Q0FASExNZtmwZ7733HoGBgSeUHzduHE6nk0WLFtVZ8mH8+PE88sgjaJp2whik+jz++OOcc845TJgwgQceeACn08kzzzyDr69vnSfOI0eO5NZbb2X69Ols2LCBM888E19fXzIyMlixYgXJycnccccdzJ07lzfeeIOLLrqITp06oZRi9uzZFBYWMmHChNr6kpOTWbp0KT/88ANRUVH4+fnRvXv3U3oPzz//fF544QWuueYabr31VvLy8nj++efrvXN//O7il19+SadOnbBYLA3+oHnqqaeYMGECY8aM4S9/+Qsmk4k33niDHTt28Pnnn7v1BLYxOp2ORx99lNtuu43LLruMG2+8kcLCQh599FGioqLqjNPxlLufsRBCtKYXXniBUaNGMXToUP72t7/RpUsXsrKy+P7773nrrbfw8/PjkUceYe7cuYwZM4aHH36Y4OBgPv30U3788UeeffZZAgICPDqnOzHK3Xjnjquvvpr777+fq6++mqqqKqZNm1Zn/4QJEzCZTFx99dU8+OCDVFZW8uabb9bbDTU5OZnZs2fz5ptvMmjQIHQ6HYMHD27w3Hq9nqlTp/LCCy/g7+/PJZdccsL79fLLLzNq1ChGjx7NHXfcQWJiIiUlJRw4cIAffviBxYsXu3Wdf9RS9f6RJ+8f1DwFveOOO0hLS6Nbt2789NNPvPPOO9xxxx3Ex8fXKavX65kwYQL3338/LpeLZ555huLiYh599FGPr/O+++7j/fff5/zzz+eJJ54gIiKCTz/9lD179pzQxqVLlzJmzBgeeeSR2uWrToVOp+PZZ5/l2muv5YILLuC2226jqqqK5557jsLCQp5++ulTPsfJuPsZufv78ffuvfdebDYbt956K6WlpbzyyivN8ptMdECtP8eWcNfx2Z5/v3TRcUePHlWXXnqpCgoKUn5+fuqcc85RO3bsUAkJCSfMjOxyuVRoaKgC1LFjx2q3r1y5UgFq4MCBbrfp+++/V3379lUmk0nFx8erp59+uradf/T++++roUOHKl9fX2W1WlXnzp3V1KlT1YYNG5RSNctSXH311apz587KarWqgIAANWTIEDVz5sw69WzZskWNHDlS+fj4KKB25uWGZnv29fU9oS31tfH9999X3bt3V2azWXXq1Ek99dRT6r333lOASklJqS13+PBhNXHiROXn51c7hb9S9c+WrJRSy5cvV2PHjq297mHDhqkffvihTpnjsz2vX7++zvb6rqkhb7/9turSpYsymUyqW7du6v3331cXXnihGjBgQG2Z42187rnnTji+ofZ78hkLoZRSy5YtUxdccIGKiopSgPr2228bPWbp0qVq4MCBymw2q6SkJPXmm2+2fENFh7Zr1y51+eWXq5CQkNrvp2nTptWuZqCUUtu3b1eTJ09WAQEBymQyqX79+p3wHXf8e3bWrFl1tv/xO9HdGKVU4/FOqZoZhHv37n3Sa7zmmmsUUO/SQ0op9cMPP6h+/fopi8WiYmJi1P/93/+pefPmnRA38vPz1WWXXaYCAwOVpml1vr/5w2zPx+3bt08B9S679Pv36MYbb1QxMTHKaDSqsLAwNWLECPXEE0+c9LqOn/f3ywR5Wm9Dn1tD8bS+31Duvn/HP6ulS5eqwYMHK7PZrKKiotTf//732qWfjreb/y2l9eijj6rY2FhlMpnUgAED1Pz585v8/u3atUtNmDBBWSwWFRwcrG666abaJbF+384ffvhBAWrGjBkNv/Hq5L8F6vv3MGfOHDV06FBlsViUr6+vGjduXJ3lMRvSWp+Ru/9v1vdv7vPPP1cGg0FNnz69zozWonHeEus1pX7Xr0EI0WEVFhbSrVs3LrroIt5+++22bo44jcybN4+VK1cycOBALr30Ur799lsuuuiiBsunpKTQp08fbrnlFm677TZWrlzJnXfeyeeff17v7PZCCNGazj77bHJzc9mxY8dJyx0+fJikpCSee+45/vKXv7RS637z4IMP8vnnn7N///4TJvoUorl5S6yXbs9CdECZmZn85z//YcyYMYSEhJCamsqLL75ISUkJ9957b1s3T5xmzj33XM4991y3y8+YMYP4+HheeukloGa5iw0bNvD8889L8iuEEG5asmQJ//rXvyTxFa3CW2K9JL9CdEBms5nDhw9z5513kp+fXzuZy4wZM2qXphCisrKS6upqj49TSp0wFspsNtc7Jr4pVq9eXWcWW4BJkybx3nvvYbfbG12TWgghBKxfv76tmyDaifYY79trrJfkV4gOKCgoSGZgFidVWVlJUoKNzOwTZ4BvjM1mo7S0tM625ppQBWp6LvxxDc2IiAgcDge5ublurUsthBAtZenSpW6VS0xMREYPirbWXuN9e431kvwKIYQXqq6uJjPbScrGBPz93J8BvLjERdKgVI4cOYK/v3/t9uZ66nvcH+80H/8BKbNvCiGEEO5rz/G+PcZ6SX6FEMKL+dpqXu5y/u8hhr+/f51g2JwiIyPJzMyssy07OxuDwdBsa3oKIYQQp5P2Fu/ba6yX5FcIIbyYC4UL97vleVK2qYYPH35Ct/1ffvmFwYMHy3hfIYQQognaW7xvr7G+WZLfpg6yFkIIUcNkMrXIjJ0uXLg8LO+p0tJSDhw4UPt3SkoKW7ZsITg4mPj4eB566CGOHTvGRx99BMDtt9/Oa6+9xv33388tt9zC6tWree+99/j88889PrdoPRLrhRDi1LRUrIeWj/feEutPOfk9lUHWQgghakRGRpKSktLsQdGpFE4PJmTxpOxxGzZsYMyYMbV/33///QDccMMNzJw5k4yMDNLS0mr3JyUl8dNPP/HnP/+Z119/nejoaF555RVZ5qgdk1gvhBCnrqViPbR8vPeWWK+pU5ymrri4mICAAFI3Jno0yFoIIU7VNYfGNVqmqqCcZVd/iS9+9DIOw0dreEDMtznvNmfz3FZcXExcXBxFRUXNNu7m+HfzkT0xHk+AEdfjWLO2RXR8EuuFEO5ytzttir3crXJ37ruq0TJKKXbf+j0VxTkkDb+CgIguJy0f+NNOt849+9ibbpVzR0vE+uP1Srx3X7ON+fX30+Hvp2+u6oQQolFGX1OjZXbPWI2GxiDTWIzayWcw9MYv//Y2Bkh0bBLrhRCNcbc7rc3uXqJm8G189uG8FfsozT1Mt7G3EBjdo/E6tcZ/P0DH+l0g8d49MuGVEMJrlaYVkPrDTrrq+zea+HorFwqnBEMhhBBeyuVwkvr+MvyjurmV+HoriffukeRXCOFVXA4nBTuyyFqTyrGF+7CG24jL69bWzWozcidYCCGEt1FKUZGWR8H6Q+Qt30dleiGdz5ne1s1qUxLv3SPJrxDCKyilOPDZZvZ/vBFHWTWmQCvhQ+LoOnUw+pvaunVtpzUmvBJCCCFaS+HmVA6+8DNVWUXoTAb8+8XT45GL8d0f09ZNa1MS790jya8QosMrzyjm4BdbSJm9naTL+hI3qTsB3cLQdBoAlRS0cQvbjut/L0/KCyGEEO2No7SS3KV7ODxjMX69Y+h0zwT8+8ahN9esGevY38YNbGMS790jya8QosOyl1Wz+YkFZK44jM6kp8+9o+l0Wd+2bla74vRwDJAnZYUQQoiWppTi6KerOPrZapTDRdi4XnT+8znoTJLG/J7Ee/fIvxohRLvTeVHD43Zc1XbK1myjcscBqld/hFIuki2jCTfEY3jfSNX7ZSccM7/y05ZsbrvmVDUvT8oLIYQQTZXicG8Jo0k/3H/S/RW791KxfTfOVe9Sbi+gU9Aw4gMGYEnzgz+fWN51ZLPbbfy54mO3y3YUEu/dI8mvEKLDcOQVkf3Kp1QfOoYpMZpYY1dijd3x0fm1ddPaLekGJYQQoiNRDgf5s76jdMUaDKHBRFpj6RNxDsHWuLZuWrsm8d49kvwKIdo9R24BJUvWU7JoHZrJSNSjd2DuFEu3O/a1ddPaPRcaTjSPygshhBCtzVVZSdn6zZQsW4k9J5fgay/Hb8QQer6U09ZN6xAk3rtHkl8hRLvlqraT//FcSpduQDObsI3sT+Al49AH2Nq6aR2GS9W8PCkvhBBCtBalFKUr11Iwey6quhprn56EXH8l5gR50usJiffukeRXCNEu2TNyyX71MxwZuQRfex62swejs5jbullCCCGEaCauyiryv/iGsvWbsY0YQsB5EzAEBbZ1s4QXk+RXCNHulK3ZRu6736IPtBH16B2Y4qPaukkdltPDblCelBVCCCGaqjo9k5x3P8ZZUEjotGvwPWNAWzepQ5N47x5JfoUQ7cpns4vJee0LfIYlE3rjxeh8LG3dpA5NgqEQQoj2Ji3VQeZ/X8MQFEjUX+/FGBne1k3q8CTeu0eSXyFEq+j+wR2NlqnOzuLY+28SRjR91/ZAW7fnpOV/dn7ZXM3zWi6l4VIeTIDhQVkhhBCnjwez3Hsy+9OXw06631lVybHZMzE6yhgdcgOG781AcYPlXUfS3TrvfC9cvsgTEu/dI8mvEKJdKNm2hezZX2IMDKR7yUA07fT8Um5ucidYCCFEe1GVk8nR2TNxlBYxMP4yDHqZy6O5SLx3jyS/Qog25bLbyft5LkWrl2PrO4Dwi6/A8rj7C9WLk3Oiw4nOg/JCCCFE81LKRfHOTWT8/DWmwGASp/2ZkLXGtm6WV5F47x5JfoUQbUI5HOTOn0vx2lUopQidfDEBw0bJE99mpjzsBqVO025QQgghWkbR9g1kLf4eZ3kpAX0GEznpUnQmM1DQ1k3zKhLv3SPJrxCiVSmHg9IdWyn4dTHVOdkEnz0eW98BmMJksouWIN2ghBBCtDalFBVHU8hbu5TS/Tvw7zWQwAHD8InrLDe5W4jEe/dI8iuEaBXK5aJ022byFszDUZCPtXNX4i67BnN0TFs3zas5lQ6n8qAb1Gm66L0QQojmUXEsleylcylPO4gpOIzoKdcR0HtgWzfL60m8d48kv0KIFpWT6+DL70pJe+Ep7Pl5+PbsQ9T1N2GOlLV7W4MLDZcHY4BcnKbRUAghRJM5qpykrMhg8+f7ObIhB3NoJLGX3YStS080zf0YJJpO4r17JPkVQrSI+UvKeOXdQhYsKwfA2rMfEVdNxRIb18YtO71INyghhBAtpSC1hHUf7GH/wqNUldqJSg4m5uIb8OuWjKaTpLc1Sbx3jyS/Qoh6rax0uVXuoQdur/O3cjk5tHUOmSn7sQXFkdBnEqGxfTEZbZAOpDc+v+ACWb+32XjeDer0vBMshBDepMBV7la5l/LOcLvOr1fWLVu6bhN5n/6C3s8P2+izCBncH2NkBPHzFex2AY38jkh1b/1e5bC7VW6C7nK3ygEscM1yu2xHIfHePZL8CiGaTWnhMVJ3zqMwez+dB1xKROLQ3ya2cC+XFkIIIUQ75iwqpnjRrxQvWobvsMEEX3kxOpOprZslhFsk+RVCnBKlXBRm7ePY/mUU5RzA7BNEr+HTCYrs0eix9qpSivMOYwuKw2wNaIXWnn5qxgC537XJk7JCCCFOD0opqtMzKVmynNJ1G9F0egInn4P/pLGNzt7scjkpyN6DxScEH78Ime25hUi8d48kv0KIJtm1uZIPXs5nw8qHcTqqsAXG0m3ItYRGJ6Pp9CeULyk4Ql7GDkoLjxLT5Sz8Q5LYvvItyoszAbDawgmJ7kNgWFcOHTpEp06dWvuSvJLLw0XvT9cJMIQQQpyoJKeSZa/t5uDKLEqyv0Mf4E/gBZPwGzkMnY/1hPKOgkKO7N9IcUEaASGdiO18Jod2/EDG4VUAGM02gsK6ER47ELsjhwBDWGtfkteSeO8eSX6FEB4pK3Hx5tN5/PB5MV16mojtPo6AsM7YguLrvZtbVV7Ahp+frLPNLzCO1F3zqCjNpc/IW7FXlVGUe4Cj+xZzdN9iOnd+C7vdjsEgX1GnSsYACSGE8JRSig1fpLDklV0YTDr6XZTAAfMorD27odUTm5XTSdar71C1/2DtNqejiorSHDJT15LU63x8A6IpzDlAzrHNZB/dBMBQ22QCDeGtdl3eTOK9e+SXpRDCI68+kcuSuaXc83AoF13vzz8fHHvS8kW5h+r8bQuMJTN1HfaqEszWIHz8IjCF+RMW25+i3BQqSrP54YcfJPFtJi50svSBEEIIj+yaf4z5T21jwGWJjL2vF1Z/E+nLezVY3lFQWCfx1RvMmCz+ZKauBcAvKJ6AkCSCwroCcPTAEjqbB+CvD23ZCzmNSLx3j/y6FEJ4JCPNTnC4gchYA0X5jc/cHB4/iNDY/mhOF2XFmaAUW399FYCqikJ2rfmAzv0u4fCun6gozabXsOlccMEFLX0Zpw2n0nAqD5Y+8KCsEEII71R4tGa26PhBIZTlVWHxM560vDE0hPhXngZNI/TrTIxmG+sWPgWATmdg59r36TPsJrKPbiYzdS3RSSPpUtD43CDCfRLv3SPJrxDCI9feEcRj92bx0C01Y3V9/P9LQHhXrL4h2KvLcVSX4bRX1pZ3Oh0ol53YrmPwD05EKRd9Rt6Gr38kVRWFbF32GluXvYLFN5QeQ6YSHNnwnWXhOaeHY4Ccp+mdYCGEEL/pe2E8O348wncPbQTAL9yCK+kopvg4VHUVrtIynGUVoGqWclBOF6qyEmufniQEDAOg/+g/odMZMFkD2PLra2xd8QZ6vYmEHpOI7jQKVh5q8PzCcxLv3SPJrxCiXv1M1fVvH2/gpu3RpGc4WbW2iiUrivl1xSrSDjsJDtYREqzDL0jH8eG/JpPG6rVVpLkc9Bp1C6DDP7ILAD4+NpLH3EVlWR7BUb3R6Q24gD4PvuhWG929aRn9/Bq3ynnj+sIupcPlwRgg12k6BkgIIbxJkM7HrXJTAjbXvyMArl0cTHFRADs2VbN+ZSXLV+zmyDcb8fE34BdkwNdfj05fE4h1eo3MrEoKd+8m9d3BaDodEFVbXfDEm6ncsQ9rvx4ofxvHAJ/+vd1qo3+Ke2slBs7f61Y5gHMCbnSr3M9F77tdZ1uTeO8eSX6FEB7T6TRiYwxccYmBiy+pme1RKVXvhFdKKcZOyuFYQf1rANqCYrEFxTZ4LqVcVBfkYgoMqXcW6dbwzDPP8OKLL/Lmm29y8cUXt0kbmkruBAshhGgq/wA9I8ZYGTHGyqSqyAZjPcCXz6Wx6LMsqGe/Icgf2+jBJz2XvSgfncUHvdnSLG33VKEjm00VC4kxdqWbeXCHW5JJ4r17JPkVQjSLhoLE0WNOtu+0033YQACqK4rJTFlDTtpGqsryAUhIPp+YbmfXe3zuuiVk//oj6HTYErrhE9cZS2gkeosPlbkZlKenUpl9DFNQGBFnT8YUENys12Q0GrHb7QBcc801LF26lKFDhzbbOVqaC8/G9bh3f10IIcTp6GQJ4caFBYyYEspBTcNVbad8zRZKFq/Fnp6NqqrG2q874fdPr/fYioyjHH7/BQAs0fH4JnTGEhWHwS8Q++E8SvPSKM1LQ9PpiekznoDIbs12TVvKl5DlOIyGDoWLw9U7MGtWEs19mu0crUHivXsk+RVCtKiUFAcAPgFRlBdns2XBcyeU8QtJbPB4S2hNtym/Tr1QDju5qxfgsv+vS7amwxIejTUyjtKUvRx45ykC+5xB6NCxmIJObQbJwsJCAOx2O08++ST33nsvEydOZMqUKezevZvg4OZLsluS57M/ul9WCCGEAHA6FDlHq4jt5sMBp5PMx17HfiSzThlTp7gGjzeH1ix3ZPALwBQUQtH2jeStXvLbflsItpB4qsry2bP4bfwjuxLdexz+4Z1P+QltniMdgFBDNMnWM0mp2sHeqvX46TtGnD9O4r17JPkVQrSoXXscGAxg9gmiojjzhP0RScNQTgfVFcWYrP519lXmpNeuJxjcfyS2pO4olwtHeQnO8lKMwWHojDXdqZ1VlRRsWUXe+qUUbl9H5PiLCR4wssntDgwMZM+ePUycOJHXX3+dyZMn8/DDDzNp0iT27t3L8OHDm1x3a/J83b/TMxgKIYRouszDFTjtishECzgVqtpRZ78xOhxzUhz2jByMUWF19tmLC3GUlWAMDMGvezIR46eglMJVWY69uIigfBtGiw0A5XKRf3Q76TsXsWfRDILj+9JlxHX/G2PcNGf7Xcnm8sXkOtLJcRyls7kfqdU7KXBmN7nOtiDx3j2S/AohWtTipZUMH2qmoOAI9spiAsK7UpS9nx7Dp5NzZBNZKWvISqmZkGr4Jc+gaTqUy0nat+9Temh3bT06kxkATafDaAvAaAuoM+GV3mwhdOhYggeOInPxHLIWz8GW1ANTYEiT2969e3dWr17N+PHjuf766xk4cCCJiYkMHnzycUtCCCHE6WTbr0UYjBr+IUYqFuzBb+xQCj7/kaCrz8dZWELxvF/JfuEDACIf/hPmzjVPgXNXLSJnyY+19dTGek1Db/VFb/XFWP5bB11NpyMkvh/BcX3JO7yJg6s/Jzd6I2Gdzmhy2/WagYE+49lRuZwdFcupNlfiwkmkIbHJdYr2S5JfIUSLSko08N7MMuD12m0RnYYTHN0Lp7OKvKNbMVn8ie9zXk3iq1xkr1xA6aHdaAYDylFz99gcEu7W+XRGE5FjLqT04G6OzH6P6HOvOqX2R0dH07t3b77++msCAwMpKyvDaDz5eoftiQsNF56MAepYE3wIIYRoexEJFhx2xT+nbAe2A2CIDMVvwggcOQWULFkLLhe20YMxJcUAUHpoLzlLf0Lva8NZVgqAOSzSrfNpmkZo0iAKju0ideP3AAQovyZ3gdZpOoL1UWTYD1HmKgJqkuKOROK9ezrWpyqEaDU2XfPMtvjMX83EhRXSr7eZkWdYSUmzs33PQd795F/s31jJNZf48d6L4ezcu5J5ixbwzidF5B6rSXiPJ76TJ/ky5+8fenTebZMDuPHeLLZ++hK5E0YQOOVs9AG2kx7T6fMnT9hWvGgdeV9/h2YxsaNHKLlLc4h6+BYyHnvHo/a0FekGJYQQoiGDTPWvxFCfPsasBvddMlmRVBSMwwEjLjCSm+Pk6BEns2c9ztKFlcTH6vnqu1CKCg+wfu0zzPqinC0bayaTPJ74+gdoLHhyHjbb/Dp160+SpBUUuHj4YcXsb78kf0A8ne4Yg2+nsAbLH5e1KrrO3xVHUzny9sqa890yGd7Yy45BhZzb7a+N1gUwb98zbpVrSRLv3SPJrxCiRQUF6vnX/b91Pd6+p4qb//zbOJrv55fim1SCywVWi8aVF/nxydfF/C/vpVtnI++/GOHxefv2MrNmXhyvvFvIg0+tp+SX1Zi7J+B39hnYRg2o9xjlclF18CjVqRlYeiRijAkn773vAIj4v6lYuidQsmAN+R/9iPMRJ3p92yy95AnPlz44PYOhEEKIptPpNG6ZWjNvR5qjCl+bxmUX5FJVVbM/7bCTUYOzOD5f5YjRJs4YYmT9upoE2GSCDz8KwmbzLAYFBel49dUgLrmkkpv+r5BNt36INT6Y0NHdiL92GDpT/alOdW42FUdSMdhs+HTpQd7CnwAIHX8e5shogs8cT8HKJZTHdsLHGNCEd6T1Sbx3jyS/QohWNWKwlcf/GkJOnhM/mw5/v5pXnx4m+vc24+OjIyHWwIJl5QwdaOH+24MIDmpakmkwaNx/exCvRN5G+abdlCxeR+6MWVTuSSHoiono/X97EmzPzCPnlU+pTvvfpFyahrl7AgDWAd2x9kwCIPiGC8h45C1efvll7r///lN7M1qBS2m4PFn6wIOyQgghRH1MJo0n/xvIxvXV2Gwavr46fG0aCYl6eicbCQ7Rs2JhBfbqMnr0MHDl1VYGDXL/KfQfjRljYfD7V1KwKZXcX/dx5NM1FKxPoev9k7B1+W3YlKvawd5n55G7dG/tNnNkDFVZ6WhGIwFn1EyUGTx6HMVbNrAr+xcGRV/WIdb8lXjvHkl+hRCtymTS+Pt9J18+4OEHQnj4gaZPVPVHej9f/M4ajO3MQZT8spqCrxdQumwj1r7dCP/ztWgGA0U/LceRX0zkP2/C3CmWkmUbqdyVQuDl4/GfMKy2LkuXOPzPGc4//vEPLr74YpKSkpqtnS3B5eGd4NN16QMhhBDN6/wpVs6fYm1w/8SJFiZObJ4hVgA6k4GQYZ0JGdaZiAm9OfDKQjbf/hHWuGB6P34x1tgg8tccInfpXsKnXI5fn/5UHkmlaNNarImdCBg8HL21pr06s5nwKZeR/sm7pJfsIsa/d7O1s6VIvHePJL9CiNOCcjhxlpThN3E4vsP7UvTjcop/XE71kSzMSTFoeh36ID+svToBEDBpOAGT6l/OKOiKCZSt38drr73Gf//739a8DI+5lA6XB+N6PCkrhBBCtDdVuSUE9I1l4Ns3kLt8H/uemUfur/uIu2YomrGmJ5mte2/0Fiu+XXvg27VHvfXYuvUi3LcLKQXrOkbyK/HeLZL8CiFOC/mfz6Nk/irQ6/E9ozflW/diiArFGBUKgDEihJIl63GVV6LzOfmdaJ3ZxMUXX8z8+fPbffLrRMPpwYyOnpQVQggh2pP8NQfZ+c9vAQgZ0YXytDw0vY6gMxIBsEYHAlCZfgRb98YT2mi/XmzJ/J5KRwkWg19LNbtZSLx3z+mZ8gshTju2Ef1r/sPppGzNNqzJXYl6+HZ0lpo1BY3RYeBw4iqvdKu+oUOHsmvXLioqKlqoxc3j+J1gT15CCCFER+TfJwadpebZXt6qA7gcTvq/di22rjUTZ1pjgwBwlpa4VV+ApWZW6OLKhme6bi8k3rvn9LxqIcRpx9w5lrgZ/8Q2eiDo9VRs2Yum/+0r0FlaDoDO5uNWfSEhISilKCsra5H2Nhcnv90Ndu8lhBBCdEwGm4UR391D0q1nYfCzUJVZTFXOb4muo7TmBrfO6l6sN+lrxgA7XNXN39hmJvHePdLtWQjh9f4zdM5vf4wzsG15Z565cR/3dZpDREJNF+dZq46yMkTH/nM+dmtWx2HJNd2lLu/715N2hZqX9tKpNP2UyRggIYQQzcGsGd0q19XoXjl3HXO695R2Wtzq3/54ABx3D+DOAWsYUL2FMXE1T273ZRWxBhh0fgHB3XY0WuehdV3hIFREWSmLD22w3ATd5Y3W5VD2RsucCon37jk9r1oIcVorKahZRNhg+u0rcO/GEoYNtri9nEGFoxjQMOiab6bKjuyNN94gKSkJi8XCoEGDWL58+UnLf/rpp/Tr1w8fHx+ioqKYPn06eXl5rdRaIYQQ3q6s2IHLperE+v0bSzD76AjsFORWHZXlBQAYTb4t0saOxhtivSS/QojTSlZqJe/8PYWuA2wER9bcnd69rpjda0u45HxbI0fX2Ly9ir1FKwmzJGLQNe8d7ubmVDqPX5768ssvue+++/jHP/7B5s2bGT16NOeeey5paWn1ll+xYgVTp07lpptuYufOncyaNYv169dz8803n+rlCiGEECilePHmXRiMOpLPrEl0y4ocLPo4nQHjQ9AZGo91jkoHu9Z/iMHkQ2Bo55Zu8ilr6XjvLbFekl8hxGnFYNKwVykKc6rZuaoYpRSfPX2ErgNtXH2Je8nvZ9+UoNMM9A2Z2MKtPXUKDZcHL9WE2R9feOEFbrrpJm6++WZ69uzJSy+9RFxcHG+++Wa95desWUNiYiL33HMPSUlJjBo1ittuu40NGzac6uUKIYQQaJqGpoG9ysXmhXnYq13Mf/8Y1RUuLn0gwa06srZkUl6SRc/B16E3mFu4xaeupeO9t8R6SX6FEKeVkCgz/5nTm4BQI09N28s7f0/hyN5yhp0bjF7vXiDIzHZiMwZj7ABdnpt6J7i4uLjOq6qqqt76q6ur2bhxIxMn1r0RMHHiRFatWlXvMSNGjODo0aP89NNPKKXIysri66+/5vzzz2/eixdCCHHauv+93oy8JJxPHj3EoxduIXVHCV0H+xMc6V4iW5lXs5qDf3BiC7ay+bRkvPemWC/JrxDitJPY25e/vNUNgNz0anQGDaXcP764xIVBM7VQ65qXS2kevwDi4uIICAiofT311FP11p+bm4vT6SQiIqLO9oiICDIzM+s9ZsSIEXz66adceeWVmEwmIiMjCQwM5NVXX23eixdCCHHa8gs2cuNTXQmONpOVWgE6z2J9dWk1ms6ApulbrpHNqCXjvTfFekl+hRCnpU2LCwG4+fFEDEYNe7XL7WMT4w2U2vNQyv1j2ooTnccvgCNHjlBUVFT7euihh056nj9OFKaUanDysF27dnHPPffw8MMPs3HjRn7++WdSUlK4/fbbm+eihRBCCCA/s4r89Cqu/VcnrL567JXux21btB/K5aCiLKcFW9h8WiPee0Osl+RXCHHa+eWTLN5+KIWuA22ExZlJ7O3L3Hcy2Lar/q69f3T1JX5UOIs5Vra7hVt66pp6J9jf37/Oy2yuv5tYaGgoer3+hDu/2dnZJ9whPu6pp55i5MiR/N///R99+/Zl0qRJvPHGG7z//vtkZGQ07xsghBDitHR4ewmPTN6MLdBAnzODiO9lY8/aItZ8n+3W8ZGDojCZ/Ujds6CFW9o8WjLee1Osl3V+hRBe78OrzgWgyl7KwfSlHM1NJT58KAlM5KOrdTizQykrmsfNjxfz0LtdGq+wRwBBkf6k2Q8ScMakFm79qXGhw+XBfU5PygKYTCYGDRrEggULuPjii2u3L1iwgAsvvLDeY8rLyzEY6oYfvb6mW5nypE+aEEIIr7emMtqtco/Pr4lByu6geMUqCudtxBQZS8htN/HmDh8cgcXAY7zzf/uJ/2Aamq7xeBc8upTMRd+R3cmB3tzwPB8xPzXeNVpTLnC6dSlN0pLx3ptivTz5FUKcFhzOatbsfovMgp10j51E99hJ6LSar8DI4D5YjP5sXlrMly+l43I1/qXs6x9FWXEG5cX1j3VpL5xK8/jlqfvvv593332X999/n927d/PnP/+ZtLS02q5NDz30EFOnTq0tP3nyZGbPns2bb77JoUOHWLlyJffccw9DhgwhOtq9HzlCCCFEfXI+/oyCOT/gO7AfEXfcjN7XBwC9vx++gwYAkP3CTFxlFY3WZQ6LAuWiZO+2Fm1zc2jpeO8tsV6e/AohTgvpeZuptpcxss/d+JjrLm5vMvgwOvk+Qse8yxcvpFNV4WLqQ7EnrS8iaSh5GTvYvuwNBp3zdwzG9jnz8++7Nrlb3lNXXnkleXl5PPbYY2RkZNCnTx9++uknEhJqlpPIyMiosw7gtGnTKCkp4bXXXuOBBx4gMDCQsWPH8swzz3h8biGEEOK46qxsyrdsI+Sqy/EbMbTOPk3TCLvhWmzDziBn5sdkPf8+EX+7BZ254QksfWKS8OuWTPq8LzAGBOGb0LWlL6HJWjree0usl+RXCOH1yipzOZC+hMjgvickvsdpmsbFd0RiNGt89OQxhk0KpNvAhtf9tfgG0+2Ma9i6+CXKizPwD0lqqeafEqV0uDxYyF55uOj9cXfeeSd33nlnvftmzpx5wra7776bu+++u0nnEkIIIf7IVW0n9+PP0QcFYjtjUIPlrN27Ef5/N5H5xJsU/7ycwAvHNVhW0+uJvWga+157hLK0g+06+W2NeO8NsV66PQshvN7Wg7MwG/3pGX9uo2XPuyGcxJ5WPv9veqNlLb7B6PRGju5d2gytbBlONI9fQgghREdT+OM87BmZhN80Dc148ud75qQY/MePoPjHX3E20v1Z0zTMYVEUbFqBq9q9iTHbgsR790jyK4TwapWVLkors0mMHIFB3/jC9jq9xuDxAWQcbjzAGYxW4ntNojBrL05H+w2IQgghhLerSjuCT98+mONPPmzpOOugXqiqapyFxY2WjRx7Ic7KcsqOHDzVZoo2JsmvEMKr/bqm5o6urznE7WPKS1xobn47Bkf2QiknRbmHmtK8FudSni5/0NYtFkIIITxTWujAnp6JISzU7WNURc1N64bWqf09c1gURv8gylL2NrmNLU3ivXsk+RVCeK2ychd3/jWbQFs8Ab7u3QkuL3GyZFYuQyfVPzb4jyy2UMzWQAqz9p1KU1uM639jgDx5CSGEEB3JzCeOolD4jRzm9jHF81dgjI3EENl4wqxpGr6J3Sg73D5jPUi8d5dMeCWE6LBmlQacdP/y7/JISXMwOvJs9DlFjdb3/rFRHP1pFxUVivILLuD9Yw1PeJXf47c1/awp3ck7sp/AHo2v89faXGi4PBjX40lZIYQQoqmKXY0vNQTwRdb4k+6vzC/n1zmbiDnrUkIyg8CNFQjL+mdTufMA0fdehG9gdYPlqkJ+mwna3Lc7hdvWUqYvwBAYWKecZm58WJWmNChvvG1NJfHePadnyi+EOC2k7i4nLNaEr9G9p7gAhTsy8OsUgiWs4cT3j3yTulOdl4W9uLAJrWxZrbHOrxBCCNFWig7kAeAX283tYyr2HgHAt38Xt4+xdukKmkb5/vb59FfivXsk+RVCeK3cjGrCYhq/G/t7yqXQdJ4FBN/EroBG6aE9Hh3XGqQblBBCCG9WkVUKgNHP/Rvd6viAVw/ivd7HB3NsHOV721+sB4n37jo9r1oIcVoIiTKRl9Fwd6b6BPePoXhfDhVZJW4fo7f64pPQmeJdmz1tYotz4cnkF551mRJCCCHamjW8pqeWvbTx4U3H+fSKBw1K13k2gZVvnz6U796Fq7LSo+Nag8R790jyK4TwWpHxZnKOVeFSTrePiTizMwB5G494dK6APoMoP3yAqhw3Bhu1IvW/MUDuvtRpGgyFEEJ0TLbYmvk/qoty3T7GGOKPtXscZVs9W6nB1n8gyuGgeMN6j45rDRLv3SPJrxDCa0UmWnA54WjZTrePMfiY0FuNOEo9W7fXv/cgTMGhZM7/BqXaz/oBni17UPMSQgghOgprhA1NryNv1xqUy/2b3YYAX5xlnj3BNQYF4XfGEAp++RlHqfs9xFqDxHv3SPIrhPBa4bE1432Plrqf/AIopwvN4NnXo85gIGLixZSnHaR41xaPjm1JMgZICCGEd1Mop4uig1txVLo/nbJyeR7rAULOPQ80jfyffvT42JYk8d49stSREKLDWlbc/aT75/5rMdZgC8lFZ+DMaLw7ckFZJwBcDhdlDgu5Zb4Nlq3odeLdYn2vRHwO9ibr1+8wXtwVzWDgnKCbGz2vQ3k2LtkTnt7dPV3vBAshhGhdVs3oVrnt6VEn3V8wdxUAPYdOI7jABwoaf/qbfsgfZ4kOzWik6pB/g+Vc1np6cll9CZxyLvlfzsY2dhTmuBi0zvGNnlNzVoFn9+I9IvHePadnyi+EOC3odBommxEfzf1li5RS4FJo+qZ9PQZeOh5nQTHlG3c36fjm5sn4H0/XCBRCCCHamqaride+/pGeHeh01h7rKb8Rw9AHBVKyYlWTjm8JEu/dI8mvEMJrJZ4VR1FaCUq53D5GOWvKNqUrFIApNgJztwSKF6xuV2N/hRBCCG/ke0YPACpKczw6TrlcTU5+NZ0Ov2FDKNuwGWdZWZPqEG1Dkl8hhNcqz63A6OPZ6A5nSU13Zp3Zve5Y9Qk4/0yq9hyman9ak+toLjIBhhBCCG/mKKiZeEpvMHt0nKu8HM3s2TG/5zd6BGgaxctWNLmO5iTx3j2S/AohvNahxWkkjI5B09z/qivechgAW89Yt8oruwNnUQn2zFwc+UUopbAO6I4+yJ+ytdub0uxmJcFQCCGENytdswu9vy9+wQluH+MsKaE6MxNLgnvHKKcTZ1k59tw87Ll5KKcTvZ8Nn37JlG/e1tSmNyuJ9+6RCa+EEF4pbVU6efsLOeOOfjgXuX9c0br9+HSNxBTq13CZTYfI/HIT1YeO4iqrqLNPF2DDZ1AvTPGRlG/chUt1Q6fpm3oZp0wmwBBCCOGtnMVlFC/aiN/ovh7d6C7fXTMvh0+vXg2Wqc7JoXDJQir2HcBZUFhnn2YyYunWBWNYGPbMLEr8s/GzhDfpGpqLxHv3SPIrhPBK2z7bQ3jvEBLPjOEg7q/7V7Yvg4ChXRvcn7tgG4df/BFT5zj8zx2JISQQnY8VzWpGVVRRuT+V0uWbcBWVAnDYsoNOln6nfD1NJcFQCCGEtypetgVXVTXBl4+Bue4fV3XkCMbwcPS2+ifErM7K4tjLL6OzWvAdNABjRBg6qxWdxVKz/+gxyjZupmJHTRK9J/0XBiddi6a1XQyVeO8eSX6FEF7H5XBxdG0m/a/v6VEgqs4ppiqjEN+u9S+roJTi2Me/EnxWL/xurj/I+QzqScB5o8j678dUHzzC/soNaGgkmvt4dFe6uSjwaEZHmaJLCCFER1G2eT/m+AgMAQ0vTfhHSikqDh7EHN/w8kSFS5ag9/Ul+qH7axPe37P26Ib/2LPI/3oOJctXkV96mE2HvyA57iJMBmuTruVUSbx3jyS/Qoh2x64cbpX7cXPfercrhwPlUuxcVkze6L50Z5Nb9aV/uwmdxYRlYE8q7SdOeFW+/xj23BL8xw6k6/STj/HpqoZwQLOSpvaxr3I9/n0Uy5Ytw8fH54SyxcXFBAR87FYbPSV3goUQQrRHx5wVjRcCnGkNJ7bVqbk4i4up3uN+7CrM2Y89K4uosy7CknXiccrpoGLnbgL7nEGvFzNPWpdSA8gI9GVb4QJySw6yZNd/2blzJ73q6U5dE+ufc7udnpJ47x6Z8EoI4XUKv50Pej1BV012+5gqVUneT+sJmTIUvU/9sz/mfrsaY3ggvsmJjdan1wx01w9g4cKFAGzYsIHvv//e7fY0F5kAQwghhDcq37MHZ3ExQeedh76eG8sNSdvzC9aoeHwTutW7v3DnBpzlpQT2OaPRujRNI9qnO6mpqYSH14z5/c9//uN2W5qTxHv3SPIrhPA65eu24Dd2OOYuiW4fU0EJqtqBtVtMvfurjuVRvGoXYZeMQNO7/9U5btw4Xn31VQCOHTvm9nHNRYKhEEIIb1S6ZQvGyEgCx451+xilFGVF6Vij4usduqRcLvLWLMavazLm0Ei3642Pj+fXX38FYMmSJW4f15wk3rtHuj0LIbyKI68AZ0Ex5q5JHh0XQAg+PWLJeGsevj3j0NvqjtnJmb0SQ6CNwHH9PW7TjTfeyLZt2xgzZozHx54q6QYlhBDC2yilqDp8GEuXLh7N7aFpGjFdzuLI5sX49+iPT2ynOvuL926lujCXmMnXe9ym7t2789xzzxEUFOTxsc1B4r175MmvEMJrKLuDgi/nolktWLp3avyA39E0HbF/uRRneSWZH9ZdG6k6p4jCJdsIvWg4OpPn9wx9fHx4++23GThwoMfHniqlNI9fQgghRHtWvGIF9pwcfJOTPT42vvt4rNEJpM/7EuX8bY4RpRS5axbim9gda1Rck9r1l7/8hZtuuqlJx54qiffukeRXCOEVqg4cJvPJ16nYtoeQaZej83V//M9xprAAwq8+m4KFm6k8klO7Pffb1eh9zARNGtScTRZCCCGEBxxFRWR9/DF5c+bgP3o0Pt27e1yHptMTNfFSqgtzKdi6pnZ76cFdVOVkEDp8fHM2WbQzkvwKITq8wu8WkPX0m2g6jci/3YHPoD5Nriv4nEGgaZTtSAXAUVJBwYJNhFwwBL3V1FxNbjUuNI9fQgghRHtTvncvR555hsoDBwi75hpCL7qoyXVZwqLxje9CWer+2m1565ZgjUk8oSt0RyHx3j0y5lcI0WG5qqop+Pw7ylZsIODCCfifPxZNd2r39HRGA+aoYKr+9+S3aOk2cCmCzxncHE1udTIGSAghREemlKJk9WryvvsOS9euhF97LXrrqa+law6NpPTQHgCq8rIoP3qI2Atv8GgMcXsi8d49kvwKIdqdb8rCGi2zbXkRumfeQeXm8v777zN9+vSGC7s5/CbhvWdRLhdOh0ZViUZxji/Fu3IxxkZRbg+H33pCs93+hXuVtjFPx/WcrmOAhBBCNI8KVeVWub+mXdRombL0YvL/8zpFBSlExw+nc+z56FYYAccJZc3ZZW63Mb97IEq5asb8alCZXbMag29iN+o8EHWceJ76TNBd3mgZh7K73b6mkHjvHkl+hRAdzqHtpbxw+z7OHD2WRYsW0aVLl2aru2TBChyZOQRfdzEAztJydP5+zVZ/a5M7wUIIIToie2kVKx74CXulRvLgmwgOrX9d3qaoyDxCwZbVhA4dB4CjvBRNr0dvOfUnym1F4r17JPkVQnQohTnVvHDHfuJ7+jB37lwsFkuz1e0sLaPo+wXYxo3E0qMzAPpAP6p2H0Q5nWh6fbOdq7XInWAhhBAdjXIp1j66iMr8cvoPvgcf38Z7hHkia8n3mIPDCRs5EQCjzR/ldFKVn4M5uHnP1Vok3rtHJrwSQnQYSine+XsKygV/fqNrsya+AOVrtqAcTgLOH1u7ze/MoThy8ylZsKJZz9ValIcL3p+uwVAIIUT7ceCbHWSuSmPoo+ObPfEtq8ylPO0gYSMn1d7UtnXuhdE/iMwFs1HK1aznay0S790jya8QosNYMSePLUuLuPHxRILCm3/mZWW3o5kM6P1ttdtMibH4TRxN4TfzKJwzH1dFZbOftyUpQCkPXm3dYCGEEKe10qNF7Jixls6X9CZyWHyz1+901YzjNfoH1m7TGU1ETbqMssP7ODrnQ6rycxo4uv2SeO8eSX6FEO2eUor5H2Xyzt9TGHlhCIPGBbXIefTBgaiKKpwldSfNCLz0XPwnnUXJL8vJfuk9lNPZIudvCbL0gRBCiI4id1sGS+/8DkuwD8l3DGuRc1hM/gBUF+bW2W7r1JOYKddTkZ5GykcvUV2U3yLnbykS790jya8Qol2rqnDy5l8O8dHjaUy4Lpxbn0pqsXOZu9es7Ve5e3+d7ZpOR+Bl5xL+wC1UHzpC6Yr1LdaG5nZ8DJAnLyGEEKI1KaU4MGs7y+76AVtcAGNmXITBx9gi5zIZfDCHRlJ2eP8J+wJ69qfzzQ+iN1vIWvxdi5y/pUi8d49MeCWEaLdKCuw8OXUvWamV/OmFzoyYHNKi59P729BMRpz5RfXuN3dOwNqvJ2W/rmvRdjQnl9LQZPZHIYQQ7ZRSio1PL+Pw3D10uSKZvn8ahs7QshNMGgNDsBcX1rtPb7YSMnQMmQvnUBUyALPOp0Xb0lwk3rtHkl8hRKuwK/fWygP44IbJAOw++B3ZeYqBve9g79eR7P26brlrVrpX37k9H3KrXNXYXFS1HUtiZ7TK+gOvbcgQct6eyZDu0wiyJTRa5/yNj7rXyBZyfGyPJ+WFEEKIpnq9oJdb5VLf6gpAfupWDi/bQ+KIKwmyDOHIe3XLBS3f6lZ9rv7uLYXkcFRSlrqf0GFjaajnb0CvAWQt/p401366Bp/ZeKU5uY2XaWES790j3Z6FEO1SSVkmGTlbiIsajp9vZKucs3jpCkxxMZgSGp5gw9qnF6b4WLYe+pqdqd+Tmr2mw84M2VFlZWVx/fXXEx0djcFgQK/X13kJIYToGJz2Ko5tmY8tPImwLkNa5Zzp2ZtQTgeB/RoeU6y3+hJ8xpkczF/N1owf2JuzlGpHeau0T7QsefIrhGiX9h6ai48lhIToUa1yvrLqAioP7SHkuivRtJpbwcrlomTZSkpXr8MQEkzINZej97MRcs3llL3yJSUV2RzL24JBZyYmdECrtNNT3rju37Rp00hLS+Nf//oXUVFRtZ+XEEKIjiVr969Ul+bT5ayprXI+pRRHMtbg36M/Rpt/7fbSlL3krlyAy2EncsLF+MQkEjZiArrtqZRV55Ndup8KRxH9oy5slXY2hTfG+5Ygya8Qot2pqi6hsCSNnp0vRK9vmQkv/qig/AgAPgP61m4r/P4nihctw2dAPyr3H+DoPx9H7+uDzs+PYFMAUcHJpGavIatwjyS/rWjFihUsX76c/v37t3VThBBCNJFyuShI20FAbC+sga3Tw6uquoSKynxCe1xUu6300B7SvnoHa2wiAIc/fhW91Qe91ZcAbET4dQOlOJS/FpfLgU7XPtMnb4z3LaF9fnpCiNPW/oN21m59HYPBQmiQe+N3mkNZdT76oEB0ZjMA1UePUbxoGYEXnkfA+DE48vMp37YTV0UFzsJiKjansfXQLCKD+pBdtBelXGha+xtJ4o0TYMTFxaFO18FKQgjhBZx2F3vmv055/jGi+01otfOWV9SMzTWFhAHgcjjI+HkWvkndiL/iFpTDQdGuTTjKSnGWl8LuY+zP/ZUY/z44VTVFVZkEWWNbrb2e8MZ43xIk+RVCtCtPvFCApukZ3u9OzCa/VjuvUW/BVVSGq9qOzmSkaOFSDCHB+I+pmejCEByM/9mja8t3Kctn0ZanqLQX43RVU1ldjNUc2GrtdZc3ToDx0ksv8be//Y233nqLxMTEtm6OEEIID22em05pzmG6T7gN/6jWu9FtMFgAcJQUYw6JoHj3ZuzFhcRfeSuapkMzmgj63Vjg+OIs1h35nAp7MQClVbntNvn1xnjfEiT5FUK0G9/NK2PWd2V0TbgAs8m/8QOaUbitC/tyllK5bz+WLp2o2LqdgPMmojUwgZJeZyDIlkBB6WFs1ggsrdxed9UEQ0+6QbVgY5rJlVdeSXl5OZ07d8bHxwejsW7X+Pz8/DZqmRBCiMbkppYx/5X9BMb2btXEF8DPNwqzyZ+SAzvwTexK4fb1+CZ0wRwS0eAxIb6J7M/9tfa/2ytvjPctQZJfIUSr6Lnk5pPuL125lZwZX+M7JJnY9Va0Izua7dzOg6mNlrEohQVffN5Zia+2E+VwkDzPiPXnTfWWn1/5KXfckc2MGTN4/sVHue2225qtvc3JG8cAvfTSS23dBCGEOO3ssZe5Ve6tbaMb3Fd9JIuMJ99H5+PHSEZg+fVoo/U5yt2bZVm3cY9b5UKd4RRt2Uno0c7syjxAn6DxxH5yuN6yzqwc/F01N8FtBGI+WoyTYrfO09q8Md63BEl+hRBtrmTpBnLfnYNt9ABCb7kY3fTmS3zdpWkaIbpIUp17AQjXxWHVfE96TM+ePQGYPHlyi7evqdT/Xp6Ub+9uuOGGtm6CEEIID1UdTifjPx9gCPYn6u/TsTxd2SbtCNFFccx+gF8zZ2LW+RJp7XLS8pb//RaI17fuU2pPeWO8bwmS/Aoh2lT5lr3kvjsHv7FnEDJtMpqu7SaN6mboT7m9hCoq6WU4o9Hyd911F9OnT8fPr/XGJnvKW+8EO51O5syZw+7du9E0jV69ejFlyhRZ51cIIdohR14hmc99giEskKi/T0dv8wHaJvmN0MWTaBvAkbId9AuZhEFnOml5H82Psw2XYtROXq6teWu8b27tb2pSIcRpwVVtJ++Tn8h6/mOs/bq2eeILYNTMDDaOY6TxPEyapdHyOp2uXSe+wG+3gj15NcEbb7xBUlISFouFQYMGsXz58pOWr6qq4h//+AcJCQmYzWY6d+7M+++/79a5Dhw4QM+ePZk6dSqzZ8/m66+/5rrrrqN3794cPHiwaRcghBCi2SmlKFm+maMPvgoaRD5w3f8S37ajaRo9AkczPvo2gs3uTV7V3hNfoFXifWvG+pYiT36FEK3OUVBM1vMfYz+WTfDV5+B/7og2T3yP0zQN8KK7oR7eCaYJd4K//PJL7rvvPt544w1GjhzJW2+9xbnnnsuuXbuIj4+v95grrriCrKws3nvvPbp06UJ2djYOh8Ot891zzz107tyZNWvWEBwcDEBeXh7XXXcd99xzDz/++KPH1yCEEKJ5KYeT3Pe/p2TJBmyj+hNywwXobda2blat9rg84Slp4Xjf2rG+pUjyK4RoVdXpOWQ9MxPldBH16O2YE6Ja9fz7nVtJ0HWv82TXoewUqVz2O7ZiwER/4+iOcZfXDa2x9MELL7zATTfdxM0310xq9tJLLzF//nzefPNNnnrqqRPK//zzzyxbtoxDhw7VJq+eLFm0bNmyOokvQEhICE8//TQjR470/AKEEEI0K1dlFVkvf0HF9gOE3X4pfmcNbNXzH3Xsx6YLJFAXVrtNKRclqpBDju2UZpbQL3gSAaaGZ3nuaFo63rd2rG8pXnbLQwjRXimlKFm6gYxHZqBZTEQ/elurJ74HnFs57NpFquu3GSFdyskqx09sci7FiYNilcfq6nkUunJbtW3tTXFxcZ1XVVVVveWqq6vZuHEjEydOrLN94sSJrFq1qt5jvv/+ewYPHsyzzz5LTEwM3bp14y9/+QsVFRVutc1sNlNSUnLC9tLSUkwm77hpIYQQHVXl/jSO/XMGlXtTiXzwhlZPfHOd6exyrGVL9bI623fYV7Gm+ifyXVm4lJO12V9zpLT1J9hsb9yJ920R61uKPPkVQrS4Q6l2Mp/6gMqdB7GNHkDw9eej9239rk/+WggAua50XLhI0vUmy5VKFRX01Y8iQh9HJWVss69kvX0hvQxDiNF3avV2NqemToARFxdXZ/sjjzzCv//97xPK5+bm4nQ6iYioe/c8IiKCzMzMes9x6NAhVqxYgcVi4dtvvyU3N5c777yT/Px8t8YCXXDBBdx666289957DBkyBIC1a9dy++23M2XKFHcuUwghRDMrL3fxynMlpL//NuakaGIevQ1TXOs/WfXT1TxlVLjYXr2SeEMPDBjIcB0mQd+TzoZ+6EOC2FO0nJ2Fiym259Az8Ex0WseeMLEl431bxPqWIsmvEOKUdPt16kn3O/KLOfr3GRgLXAzQn03omihYc/JJiX6xf9GcTUTXPQmACJVIrwI/dqX/RKmrCBXog4YOc7EvEd1GEPRuEeBPePXFbPrvCnb+sAY1tZo+N5848/O5sfe4de55R19pzkvxnNI8G9fzv7JHjhzB39+/drPZbD7pYTVjpX9XjVInbDvO5XKhaRqffvopAQEBQE13qssuu4zXX38dq/XkN0ZeeeUVbrjhBoYPH47RaATA4XAwZcoUXn755ZNfnxBCiFp5LvfW7gU478c/N7hPOZ3kzPiQyr15dPcdRkJJMrrnq4EjDR7jyHGvh5Xm5iz+roHdATACw8oiWb/jHTKcKRRqhXRLOg/2QETvs9HZotl3mxEdCQQvW8+RD+eQE1BO2H03oDPX7T3UeWakW+fWL9/a+HUoFzjdqq5pWiHet2asbymS/AohWkzlgaNkPvcpaBpnGMZj0dp+hsfYoH6AorD8GHHBgwA4UrCZQ7mrGUQvAPQmPYP/dia2GH+2z1iHwWKkx3X9267hp6CpY4D8/f3rBMOGhIaGotfrT7jzm52dfcId4uOioqKIiYmpDYZQs2ayUoqjR4/StWvXk54zMDCQ7777jv3797Nnzx6UUvTq1YsuXU6+VqMQQojm5ywrJ+fND6hKSSP8jmkkzWr74Sc23wj697yO9KxNhAZ1JTigEz7WUPam/MjgPjfXlvM76wyMESFk//cDcl79hPD7pqIZOmZ61JLxvi1ifUuRMb9CiBZRlZZFxlMfYQgNJPbpO9o88T1O0zTiggeQHHsBfpZw/CzhxAcPIjV3HS6Hq065nlMH0Gv6QLa9sZYDs3e2YatPQQsvfWAymRg0aBALFiyos33BggWMGDGi3mNGjhxJeno6paWltdv27duHTqcjNta9ZScAunbtyuTJk5kyZYokvkII0QZclVXkvPE+9sxsIu6/A2ufnm3dpFpB/on07noJEaHJ6PUmuiWeS1FJGsWlR+uUs/ToRNh9U6ncfZDcGV+inC35eLYFtWC8b6tYn5KSwv79+0/Yvn//fg4fPuz+BfxOx7y1IYRo10pWbiPn7e8whgcR9dDUdrW0QX0iA3qRmree/F3ZhPat28Wp982DsZdVs+n5FVTkltPn5sFouo6zFFJrLHp///33c/311zN48GCGDx/O22+/TVpaGrfffjsADz30EMeOHeOjjz4C4JprruHxxx9n+vTpPProo+Tm5vJ///d/3HjjjQ12g7r//vt5/PHH8fX15f777z9pe1544QWPr0EIIYRnqo+mk/PuJzgLi4m471bMifUvd9NeBAd2xmCwkluwD6g7n4e1d1fC7ryGnNc+Jfu/Mwn909XofdvHTXt3tXS8b41Y/0fTpk3jxhtvPOEp8dq1a3n33XdZunSpR9cAkvwKIZpZ2fpdZL8yC9vIvoTdMgWd9eRjRdsDP0tNl52SI0UnJL+aptH/3hFYQ33Z9uZa8ndlM+SfZwNt363LbU1YvsgTV155JXl5eTz22GNkZGTQp08ffvrpJxISEgDIyMggLS2ttrzNZmPBggXcfffdDB48mJCQEK644gqeeOKJBs+xefNm7HZ77X8LIYRoO87iEjKffx1DaAhRf7sHY2R4WzepUTpNj80nnPLKPCz17PcZ1JuIB28i59VPyXzkNUJvvxKIq6dkO9aC8b41Yv0fbd68ud4lDIcNG8Zdd93VpOuQ5FcI0Sxc1XbK1u8m/8tFWHomEn73ZQ1OgtDe2J010+6bA+sLhzUJcI/r+hPYNYR1Tyxl/vVf00N3JlHWbu3+GlvjyS/AnXfeyZ133lnvvpkzZ56wrUePHid0nzqZJUuW1PvfQgghWo9Siqr9hyj6ZSkoRcSfb+9QT0jt9nL8fKMb3G/p2ZnIR+8ib8aXZD4xA1PcaJISx6HXG1uxlU3TGvG+pWP9H2maVu/ShkVFRTib2D1dxvwKIU5Z5YGjHPnzy2S/Mgu9nw/hd17S7pPC38svPQyAf1LQSctFDo1j0seXEXFGDNsKfmFD3ncUV+c0+bw7d+6sdyxLs2rhMb9t4cYbb6w3GJaVlXHjjTe2QYuEEML7OcvKyXrpLbJenIE9PYPQm67rUIlvZVUx5RV5+FrDTlrOGBZMxN9vJfDSiRw9uop1618mO3s7ypPZpH6nSlVSqNyb2fqUeGG8Hz16NE899VSdRNfpdPLUU08xatSoJtUpT36FEKfEnl1AxjOf1Izv/ccNmKJPHlTao5Tc1YTaOmOLbnx2Y3OgleGPjSdsU0/2Fq9gVc4XhFkSSbINJNgc49b5qqureeSRR3j66acZNGgQixcvPtVLOAntfy9PyrdvH374IU8//TR+fn51tldUVPDRRx+16fqBQgjhjaqrFTlvf4T9WAbhd92EpVf3DnWTGyAtfSV6vYnI0GRSGimr6fUETB5D97SeHDjwEzt2fo7NN5LY2BFERQ1y+9ozXWlsd60GYJTuglO8gsZ4X7x/9tlnOfPMM+nevTujR48GYPny5RQXFzf5t5Mkv0KIevVYeX2jZZxlleTd8zpmpTG0cgimv+UC9d/dnF/9WTO3ECboLnerXPZd9c9ECOByOijdkUvgqPF8nuT+F+nHa0JxOnqwck4OCz/OYP3e2Yy8KAxbkAEfPwP9xwQR39OXLs/VnXzJUVhA5kcfUnW0Zu3D/SYz/V9+ze3zeszTu7vt+E5wcXExSimUUpSUlGCx/NZN3el08tNPPxEe3v7HnQkhxB8VuMrdKudy80t6U1VA44WA2+Y1vHbvcUop8j6dRfX+TQzoeyNB2xJhm73B8s78ArfOvcA1y61y50b9ya1ye688+SRK2a9mYe7Xg9Rrg3hh5Cdu1XlkSAgQx4F1NpZ+mMaOxbNRIUuJ7mHDYNSRNCCAHqNCeOuruomty+Ege943FG1eC4Dex5eMu4bBs3PdOm+TeFG8P65Xr15s27aN1157ja1bt2K1Wpk6dSp33XUXwcHBTapTkl8hRJMop5PMF76kWpUz1DgRk1b/eNn2zllZ84NDb/G865beoHHmZeGMujiMOa8dYfPCAqqrXJTk25n90hE69bVR0i0On5490Vt9KD+wn+wvv0BnMqLz9UVnMhFy7vnYCwub+aq8U2BgIJqmoWka3bp1O2G/pmk8+uijbdAyIYTwXsULllK2ej29ul9GUEBiWzenyZxlZRgj61+TtjFdhgTRZUgQW3/JZvF7qexYlEN1pYsFbx0mMMqMvpsR/94DMAQGYy/MI/O7z6nKTMcUHkl1ThYxV9+Mcjia+YpOD9HR0Tz55JPNVp8kv0KIJin6ZQPl2w8xyDgGX517d5jbo7xtK0DT4RPu/vqyf6TTa1xybzyX3FuzzIPD7mLr0kKWfpHFoW9m1Vl13tqlK7aBg8j56gvCL7uC0u1byZn11SlfR4O86E7wkiVLUEoxduxYvvnmmzp3fU0mEwkJCURHNzyRiRBCCM9UZ2RR+P08/CeOJapyQFs3p8kq9h/AnpFJwLgxp1RPv4nh9JtY08NIKcWRnSWs+vIYa79bQt6y+bXlDAFBRF8xjYzZnxA4eASapiPl9adP6dyN8qJ4/0fl5eWkpaVRXV1dZ3vfvn09rkuSXyGER5RSlK7cQe7Hv+DTrwshuyIbP6idyt+1juwNC4kcdh5GW2Cz1Wsw6hg0IZhBE4J5ZOZ4qo4ewVVVhSEwCEunTmR98hHmuDgsnbtw+N//arbz1ktpNS9PyrdTZ511FlCz6H18fHyHG28mhBAdSfXRdHLe+wTNbCLgnHEwp61b1DTVGZlkvTsTS7eu2Ab2b7Z6NU0jvo8/8X38ye08jsqjqTjKStCZLfgmdaV4xyZc1dWEnDWJ9K9moqoqm+3c9fKieH9cTk4O06dPZ968efXub8qMzzLbsxDCI/lfLibzpVn4Du5OxF2XtHVzmszlqCZj5fcE9TiDsMHjWuw8hsBAfPsk4zdoMNbOnWu67RqNOMvKweXC2MQxK+5SyvNXe7d48WK+/vrrE7bPmjWLDz/8sA1aJIQQ3qVi7wEynn0FzaAn8r470Jk70Nr2f1Dw4zwM/n5E3HQDml7fIufQmy34du5OQN/B+HXvg85kRjMYQblwFBViDo9qkfP+njfG+/vuu4+CggLWrFmD1Wrl559/5sMPP6Rr1658//33TapTkl8hhFucpRXkfbmY/K+XEXL1OKLuvwJDgG9bN6vJ7GXFOCvLCezh/qyNzSXo7LE48vMoWLyI2Hv+TPB5LTgDpBcuffD0008TGhp6wvbw8PBmHRckhBCnG+V0UrZhM7nvfYylayeiHrwXU5x7Kxm0V9Xpmfgk90Znad25Sfx69sUYFELm3K8ImziFiClXtuwJvTDeL168mBdffJEzzjgDnU5HQkIC1113Hc8++yxPPfVUk+qU5FcIcVLK7iD/2+Wk3P5fCuasIOjCUQRdcmZbN8stDuWgWtXfzUhnNAPgslfXu78lmSIjCTn/AopW/IqrshJb3+SWO9nxblCevNq51NRUkpKSTtiekJBAWlpaG7RICCE6vvLtu0h/7DlyP/gMU0I8oTdeh2Zs/yMklXJR4SxpcB1ezWTCVd36sV5nNBF9+TSqMo5Sunsbtu59WvaEXhjvy8rKaldxCA4OJicnB4Dk5GQ2bdrUpDrb/79oIU5jgzddcdL9SinyF24j9731uJx2+pwxncN752Mw+tCt32X1HlPYyb3/7RNmrCPTlcoh504qVAlx+m4k6Xth/tkKP++tLTe/8lP3L6iZ5d3W8BJGZVmp7P/hDZTDztCNv3D7XTZ69Dbi56dhMGhsWFvNde/Bv66ew6gza8aS7G945YYTvH6w/vf3j+J/rj/gRjsGsUr7hZyXZlBenuP+iT2kqZqXJ+Xbu/DwcLZt20ZiYmKd7Vu3biUkJKRtGiWEEH+wpsr92X2vW3nnSfdX7k2l6KcVlG/cTeeJN1Oek0Zh6g66T74bneHELsl2m3vn7fFzNgUVRzmUu5LcshRCfJPonnQh/ioCPqgEfruBPG+Xez1r3F2GcJK18SUVAbKnD2xwn9Nexb5ZL1BVlIPhqWCCLjoTn0Hd0VnN6ExGnGUVuMpL0AVq6CIr3Drf7728eaxb5RJXVDWwJ5TioC7kzv0Ou71lfy95Y7zv3r07e/fuJTExkf79+/PWW2+RmJjIjBkziIpqWldySX6F6KCUS5H2yo/kL9hau23Dsv/W/ndDyW9jXPYq8neuZV/1L1RSTqgumn6Gkfjpgk65za2pLCsV5bATO+piSgp/YPo1+QDo9RAYpKOwwEW/AUaGjWj9cUwul4PcnF04ndUtmvgCXjn741VXXcU999yDn58fZ55Z0wth2bJl3HvvvVx11VVt3DohhGhepSu2kDPj69pBmgfnv1O7r7IwG59Qz1crUMpFyaFdHEmdT2HFMWymUPrHXEyEX/cONZmgvaSAqqIcgnsOpcq/hJy35tTu0/n5oKrsaEYDAePPaPW2KaUoLEzB6azCbi9thRPidfH+vvvuIyMjA4BHHnmESZMm8emnn2IymZg5c2aT6pTkV4gOqDbxXbSN8MuGk/31agBCo/qiaTpKi442oU4XuVuXk71+Ac6qSqJ08STqe+GnC2zm1reOwKRkMjb8TEVuOgvmh7Jlk52cbCf5eS7y81wEBumYcrEVg6H5g7xSiorDOThKKjFVJ2Iy2aisLKSoKJXKigIyMjZQUZFHUFAX/PxjMehNHDr0S7O3o6Yx3jf74xNPPEFqairjxo3DYKgJYy6Xi6lTp8qYXyGEVzme+NrOHEj5+p24yiuxBkcTmJhMxqb56M2er1FfmraP9KXfUpWfRZA1loGxlxFm69Khkt7jzEHh+ITHU3J0H3Fv34c9qwD70SycZZU4C0pAp8N3SC+MkS3TK8iRW4g9K5+KimAsliCczioKC1OorCggN3cXBQUH8fEJJy6+5kbtkbRfW6QdgFfG+2uvvbb2vwcMGMDhw4fZs2cP8fHx9c794Q5JfoXogHK+X0/+gq0kPDCF4LHJxOeORK83Y7YGsGfzFzjsFezd+hX+gQkYTb6ERPRE0504w6FSCmdFKQV7NpK/cw1VBTkEJw8nfNBY4j8/1AZX1nwq8tJRTgcGqy+apjFgUMs/4XU5nOT8tJnsuZuoPJIHwF5ApzPictX0qdbrTQQHd6VP8nXYbDXLRDkclS2Y/OJ1d4JNJhNffvkljz/+OFu3bsVqtZKcnExCQkJbN00IIZpN1eF0ct6ejW30AEJvvgjn5ePxWwrmgHDyD2wEIH39j/iExWGw+BIQ1wuD5cSJKJVSKKed4oM7Kdi5ltK0ffjGdiZm/BV02+5m/+h2qro4D2dVRc11axqmqBBMUS0//KV86z6KflxB5Y6DAGQCmqZHKReg0DQdfn4x9Em+jtDQXmiahsNR2cLJL14V7+12O927d2fu3Ln06tULAB8fHwYObLgbvDsk+RWiA1FKkfXlSjI/X0HYhWcQPLZmoiQfW3htGatvKEX5KZQUHCHryAYATGZ/TBY/knqcR1VAEIX7NlF69AAVWUdQLieaTo9/l77ETbgan8jjCUTHTH7tFSVkrJtH3u41BCT2IXLgRGB3i5+3bH8GKS/8SEVaLkGjehB/23hMof64Xi3FXl2CyRxAUFASRmMrz5DtZcHw97p160a3bt3auhlCCNHsyrfsJffdOZhiwwm98UI0nQ5DkD+WQCsAJt8ATLYgKotyKEjZCsqFzmjGZAsmuMtAAgYMoXDvZkrT9lF27BCu6pqxuz7RScSdcx0B3QfUPOnd3sJDb1qIy+kgd8dKMtfNw+gbQOL466luhSfXzuIy8mb+QNma7Zi7xBF6+2WYu8QS8nEhFeV56A1mAgOTsFiCWv9JupfFe6PRSFVVVbO/j5L8CtGOFa7cQ/Z36/BLTkAzG3AUV5Dz7VrCLhpC9LQx9R6T0G08Cd3GA+ByOSkvySQ9dTVFeSlsX/sOrAW9xQdbbFcCR/VHb/XFL747BmvHvvtbWZhDxrofKU7bg6bTEzvqUkJ7D0fTWn5S+9yF2zn88jysCaH0enkavl0ia/fZIjq1+PlPysuC4XFHjx7l+++/Jy0tjeo/zOL5wgsvtFGrhBDCc6kHq8l4/B1MidHoA/1A0yj8djHmznGE3nJxvTMu+0V3pc9V/wJAuZw4KsvI3vErZTlppK//kfT1P6Lp9fhEdyJ00NmYbIFYoxKwBEe09uU1K5ejmvQ1cyk6tA17eQkhvYYTPewC9CYL1ZS16LmrUjPI+u8nqKpqwv50Bb4j+tYmZiEhEdDW8y16Yby/++67eeaZZ3j33XdrhzmdKkl+hWjHyvYeo2znEcp2HkHva8FZWU3ElSOJnnq2W8frdHpsATF063sZTqedvMwdVESb8UvshU7vXf/7p6/5gbLMFCIGjCO09wh0RjPpa+ZSmLKdcxcUcv4UK7ffbasd47tpQzWffVRGxjEnRYWKokIXRrPGldf4cNV1PgQEupc05/y8hcMvzyN0Yl8S/jQJncm73tf2aNGiRUyZMoWkpCT27t1Lnz59OHz4MEqpU+4OJYQQrS0r3UnlnmNU7jmMZjGj7HasvTsTfvdV6HwaX5tW0+kx+vgTM+QClFKUpO+nwp6Pf+dkDNZW7m3UwvL3rCd3+wpC+4wktPdILMGR5O/bSPamRTi+rsTSPZ7QG85D719z3fbcQop+XEVVaiau0oqaV7WdGedbmXxjGDGd3Fv7t/pIFhmPvYMxIoSIR27BEBLYglcpjlu7di2LFi3il19+ITk5GV/fuv+eZ8+e7XGd8itNiHYs/NJhZH+7lvBLhhEzfSzK6ULTN+1Jpl5vJDxmgNtLHXUkSilK0w8Q3m8MkYMmAHB05bfk7lxFSM9hnNFvM2++UsqObXauuMaHj94rY82qajp10ZPc10TvZI3AIB0Zx5y8/nIJb79RytQbfZl2s+9Jk+CS7WmkvjafsPMGkHDXpPY5WYgXToDx0EMP8cADD/DYY4/h5+fHN998Q3h4ONdeey3nnHNOWzdPCCE8MmS0FVOnGHC5iPnPXSiXC03XtFivaRr+Md3o4J25GlRy7AC+UZ2IHX0pAMWpu0hb9Cn+ib2x9A2ieOlmjv7jLUJvvICydbsoWbYFndWET3JndDFh6P18UE4X6xetYuGXeYy8IJDL74oktnPDSbCzpJys5z7CGB5E1L9uRmc1t9blesYL431gYCCXXnpps9bpfb+ChegATrZ+r6O0kqyvVuEoLKVHxTqsZgtbXvnm5OuZndcCjXy5BepsZnk97biqqtAMRpzVlVR39ye/jwuA3PfXYjtrBL6XXkj+3VH0VZksX7SapYsKsBFIMgMJPxBD2UGNMiAd0A3szfDupaRmreatV9fxzut2EsKHERs2GLOx7i+JoINH2FH8A4G6cPqvGoBudW69bfw5v/HZh4uLiwkIePRU3456eeO6f7t37+bzzz8HwGAwUFFRgc1m47HHHuPCCy/kjjvuaOMWCiG8WYGr3K1yd+2c1uA+5XSRN38zpTuOoJSi+lAK/eIuJvKpk3fdde7aetL9v53A5Vaxea5Z7tXngQVu1unuOr+VgQpneRkGXxvV1cUYg4Oo+t/qizmL1mIKjSD66hsJ3ueicsSZ7F75HplPf4zRbCOh5zlEdh6O3vC7hFUP1vizSPfZzIb5y1nxw16iQvqSGDkSm0/dbuFd9E52HZoPBZUMibsR6zsGwHlCG7XNOxu9Dk3Z3brepvKWeP/9999z7rnnYjQa+eCDD5q9fkl+hWhH8hZu49h7C1F2J5aEMAo0B5988kmTF/L2ZikpKaT9+SFwuYh9+lGMURGULF2BpXtXDEGBmLt0onT1OoxRkRSpfPwJZhgTqaScQEIbfEprNtroFjuBhIjhHM5cSUrmSg5m/EqIfycig/oQEdQTg95MoTOHClcxyb5noWuFccVN5oVjgHx9famqqgIgOjqagwcP0rt3bwByc+u/CSGEEO1F+aEsUp+dQ+WRXHx7xGAvLKNT2Agi/Hu0ddPaHZdycezbjyjZs5WYi2/AGh1P4ZY1lKcdwie+Ez7xnSjZt53sRd9j9O2L0Wyj75i7KM5NwT+sM3pD/Ss96HQGYsPPIDp0AMdyNpKSsZyMvK3YrBFEhiQTGZyM1RyIUi4yc7eRED0SqzmwdS/eU14S7y+++GIyMzMJCwtDr9eTkZFBeHh44we6SZJfIdoBl91B+swl5M7dgP8ZXYm9fSKmUH82nfdEWzet3dq0aRO4au5s63x9CJl6NTlvfUDG0y8Q9df7CLvpevI++5r8z2aRDxgw0ochhGru3UgwG210j5tEUtRosgt2k5G/nZ2p37H36HziwgZTXZUNgL++rWe4OP0MGzaMlStX0qtXL84//3weeOABtm/fzuzZsxk2bFhbN08IIRqUt2Ar6TOXoLdZ6Pbfafj2iAEg/K/tNBNpY04clOypedptiYjGN6kbFRlHSP3kdWIumUrQGWeiXE5yfv2ZfMcyQCOu1wRie4xza8JLnc5AXMRQYsIGkVt0gMz87RxKX8bBo4uJCO5NgF8sTlc1/r7yEKK1hIWFsWbNGiZPnoxSSmZ7FsLbKJfi8LNzKF5/gIgrRxJx+Qh09czsKOqaPHkygZPPxdKzO5qmYY6LJepv93PskScpXbuRwHMnEDb9WuyTzyH232s5xC62sJI41YUu9EGvufcemww+xIYNIjZsEBXVRaRlrSEtex1OVzXx5l5u19NWNDzsBtViLWk+L7zwAqWlpQD8+9//prS0lC+//JIuXbrw4osvtnHrhBCifrk/beTI6z8TdFZvoqePxRTm39ZNaveMmonoC68FdJiCwwBIuPZO0j57k/x1v+Lfox8hw8YS0Hco1u155B/bwZFdCyjOOUTnQZdj8Q126zw6nYHwoB6EB/XA4awiPXcLqZmryMzfjs0nkmD/Nl65wQ3eEu9vv/12LrzwQjRNQ9M0IiMjGyzrdJ7YBb0x7ftXmxCngdwfN1C0ai9J/7yMwOHd27o5HYbJZCJg0rg62/Q2XwyhITjy8mu3qepqjpJCKNFY8OUIB8jmKCPUOR4nrlZTAN3jJtE1djzVBw9g0TrALJpeNgGG0+nkyJEj9O3bF6hZ8P6NN95o41YJIcTJVaRkcfTtBYReMIi4O2RiPk8E9B5U529Np8MSHU/xzk11tuWmbUJvtJCQfD6p2+ey6een6Dv2XmxBsR6dz6A3Ex8xlLjwIVQ5SzAZfdHpOkDK5CXx/t///jdXXXUVBw4cYMqUKXzwwQcEBgY2W/0d4JMUwntlz1nHsXcWEHreQEl8m0HlgUPYj6UTMGlszd+HDpP9xjtoOEgnpbacCxdOnOib+BWo0/RYdR1kKk0vGQN0nF6vZ9KkSezevZugoKC2bo4QQjSqbF86B/72CebYEKKnj23r5nR4ruoqirauw9atDwCO8lLSPn2TqtwsdDo9LudvE0s57BVNPo+maVjMAafc3lbjRfG+R48e9OjRg0ceeYTLL78cHx+fZqtbkl8h2oijpIL0D5cQNnkwMbdNbOvmeIWKbTvRBwXiM6AfrooK8j7+AlNUJMNTBuDCQQG56NARTAR6Td/WzW0dXhQMj0tOTubQoUMkJSW1dVOEEKJRx95diDk2mK7PTkVvqX8CJuG+8qMpOCvKCBl6Nkq5yF70A/aSQvqPvx+zTxAl+anYK0sICO+KyeLX1s1tPV4Y7x955JFmr1OSXyHaiKOoHFXtwKdHbPtcH7YD0swmXGXl5H85m/KtO1B2O2G3TsPw5GEKVAE7WIcGhBNLV9WXSsoxY8GiNd8dxfbGW5Y++L3//Oc//OUvf+Hxxx9n0KBBJyx67+8v4+iEEO1HdXYRfv0SJfFtJjpTzbJFuasWUpWdQVV2OlHnXYGPNQJHdQUHN86iqrwQv5BEkvpNwWC04HRU4RsY08Ytb1neEu8HDBjg9u/iTZs2NV7oDyT5FS3ix/KGFwv/o4nWUrfKGdv5xEIAhx0lbpULfNGGUr5kBHUm9801dNo8tP6CLbF+rxeJXVT3mztSP5r9wcco37yf8PDexHYbi3lXENl3RXN47nsYC0II7jWEnE1LyaycC0BAl/4knDsVgIg317p97vn2L5rvQlqSF94JPuecmvFyU6ZMqRMgj88K2ZQJMIQQv1lT5Wj2Oi2ae3X2NbmXIOpouyXmPijq7VY533cCAYgOG0nqwnl0MVzU4JNI154NbtW5wPmlW+U6AuXmd7Vvet3A5KMlUNV3HPmHNuMTFEXiuRfjF94JV5Ui+/BmqiqKSBgwmZyUDWxb/HLtccOufr72v/VHstw697yM190q547i4mICApp/XeVaXhLvL7rootr/rqys5I033qBXr14MHz4cgDVr1rBz507uvPPOJtXf/rMJIbyUpmlYLAHY7Sdf0F64z2C00nP4dACO7lvC7rUfYjT5YunSjeKUnVjD43BUlOGs/O09Nwc339px7ZKXBMPfW7JkSVs3QQgh3Ga2BrZ1E7yKpmnEDjyP2IHnUZxxgCNr52Cw+GKxBlFelAXKhctZjcv12w0Xn8DoNmxxK/GSeP/7rs4333wz99xzD48//vgJZY4cOdKk+iX5FaINFeQfJDwiua2b4ZWO7FmI3mDG5bBTuKrmKW9F9hEc5SWYgyKoKqi562sJkbX7OoJLLrmEmTNn4u/vT2pqKldeeSVms7mtmyWEEI0qyj2Ij3/k6TX+tJVk715BeX46AbE9yD74W++t9N1L8Q2KobI4GwBbSFxbNVGcglmzZrFhw4m9Iq677joGDx7M+++/73GdbddnRJxW8jLtHD1YSUGOneoqFwDff5DLe/9Jb+OWtS2D0Yrzd7MSiuZjstgIje1HtzOuBiDm7MsI7X8WUaOm4BuVWFtO03n3xFfHxwB58mqP5s6dS1lZzRP76dOnU1RU1MYtEkL8UVmJi4N7qslKd1Be5kIpxdb1lTwwLZOyUldbN6/NGIyWOjMQi+ZjtNqwBITRdfzNGEw+RHYbRXTPscT1O4/QxIG15bw91oP3xPvfs1qtrFix4oTtK1aswGJxf4jl78mTX9Hi5n6Yy3tPZuD83XCfzr0tHNxZCcDGy80M6nd6PcGpqiwiK2sbFeV5+AfI3cjmVpx3mMqyfGxBcVhtYaDpSP/1W5Trt/FF4YPHk71hIa7qyjZsaSvwknX/evTowUMPPcSYMWNQSvHVV181OLHV1KlTW7l1QogdGyv5221ZFOT+luRGxxtIT6sJ/t9+XsY1N9nQ6drnd0xLcDqqycvYSX7mbpTr9E3+W4qjqpyio3vwi+wMgDUggqwDq+vEer/wTiiXC6e9qq2a2Xq8JN7/3n333ccdd9zBxo0bGTZsGFAz5vf999/n4YcfblKdkvyKFlNV4WLOe7l8+mIWk6eFMPLcAMqKnWQdrWbPpnJiO5vZu6WCkeemc/kUX15+KoTgIO++M5eW6uDf/ypm1Ypn0On0hIT2IDFJ1vxrLvkZuzi0bQ5V5QX4BsYSGtMXnc5A7JjLKDywlfDB4zHaAnFUlOATkUDuthXYS738CaKXjAGaMWMG999/Pz/++COapvHPf/6z3tkgNU2T5FeIVqSUYsWCcv7zl1ySuht54o1gKstdFOY72biqkv5DIGV/NS8+UcjP35Xxz6eD6dHHu2c9ri53MO+lA6ydtxSXsxq/4AQSesiShs2lqiSPQ8s+oSzvKHqjmcjkmt9RCQMmc2TbfIJiehKaOJCy/GP4hSVxYPVnVJcXtm2jW4OXxPvf+9vf/kanTp14+eWX+eyzzwDo2bMnM2fO5IorrmhSnZL8ihaRl2XnX9encOxQFVfcGca190fU+aF6wf9+mzqdiqwfMnnw33nc+X+K914Jw9fHO3vjZ2c5ufKSfMwWjR49LyE0vDdGo7Wtm+U17FWl7F3/Kf4hiST0OpeQ6GR0upqvuODewwjuPay2rDkgBACf8DiKU3YQOuAsdHrv/Dr0lqUPRowYwZo1awDQ6XTs27eP8HAvn6xMiHbO6VQ8em8Oi+aWMfRMK4+9Fo7N/7cYfs4lv41x3b2+jCf/ns/f/pTHax+HERvvnd+5Lqfio/u2cmhDAbFdxxEeNxiLb3BbN8urpCz/DHtlKfFDLyIoPhmjT00vIFtIPD3H3FJbLiCyKwC+wbEc27GAqrJCzL6BbdHkVuEt8f6PrrjiiiYnuvXxzixDtKnKShdP3ZFKRZmT137uynUPRDa4XpderzHtaj/+889g5vxUzidfubfsUUditytmfVHO9Vfno2kw69tgomIGS+LbzKoqCnE5q4npNoawuAFuJbNhg8ZRkX2U/Z//l6qC7FZoZRtQTXi1cykpKYSFhbV1M4Q47c14toAlP5Xx2Gth/PfDiDqJ7x8NGGLm6TdCOXLYwYtPFLZeI1vRgbX5fPCnzexblccNr/YnvsdESXxbQGVRNsFJAwjvMbI28T2ZsKTBGC02ts37L3lHtrVCC9uIF8b746qrqzl69ChpaWl1Xk3hnbfdRIvpsfL6k+5XSpH1+reUbbMzNPoqfrgzstE6Oy/5jM0pVZjN0GuMnoOO+sdl/OvIOW618YGYn90qN8zs3j9/d9fuBbj5hntr/9vlcnDs6BoyMjdSVlpCcHAXOnU+h4f+L5rFi/7mdp2iYTkDfusmr5yx6Nf7cbRyK/YB3euU233Lmw3WsflP0Vx7RyauVf+lesD/YTR42U0JTye16ADBMCEhoa2bIES74sK98aSr3Zzi4LpFtzdapmzNZvLe/YLYYRcyZ+tZzNl68vJn3rmWtAOZQCaWs/rxcX79/x+XOdybAyTBmudWOYDbg7a4Ve6h9HFuldvwTv86fxce3k7enrUUp+3CEhRJwtnXs3H9AMKX7XK7jT93lLXjm5Gmd2+oW1HXug9QLN27k3dsK7bLz6lTx60Xz2+wjtIH+vLNY7vZvfRjJv3pDLZUXN60RrdnXhjv9+/fz4033siqVavqbFdKoWkaTjfXiv49SX5Fs1AuF+XbD1GyZAslK7bRN/x8AiyNJ74A33xRxvszSvnT/X5Ex3jHP0mlXOzfP5eM9PUEB3elR49L8PeXia1akqbX49e7H6V7dnh03IBkC99/HM3Qc49QWb6Y7knnt1AL24gXjgESQrQde3oWZWs2U7JgOb4jBhHW60y3jsvZV8iPD64iZkAY3SZ6TzwsOLSVwws/xCcsjvizrya46+AGe7uJ5uHXdwCl2zZjz8/FFBbh1jG2EDPXPteXGdPW89U/dtI1+TLv+5y8MN5PmzYNg8HA3LlziYqKapbPzDsyDdFmnGUVFC/ZTNEv67Gn52GMDCZ0+rlE/9qz0WOVUqQVb+GXvxVx5fU+3HGvrRVa3HJKSlxUVRWTn7ePo8dWU1qSTrfuFxETM7Stm3ZaUEpRkZaCOTrW42O7JJm46Rp/XnlnG10TzkHnTUsieGEwFEK0LuV0UrFlFyVLVlO15yA6mw8+QwcQfO2FaJsa/zFampXCt3csxT/Glykvj0Kn77ij7hxVTuzlxVQWZpGz/VeKUncSmNSPxPFTvS+ZaqcqU1PQWSwYg0M9Os5g0jHhT51595ZNRMUdxS/Ie27CAF4Z77ds2cLGjRvp0aNHs9Upya/wmLI7ap7yrt5J6codKKcT27DeRNx+IZaeCTVf/r8Wn7QOl3KSWriRvfm/cv1Nvjz4L/9TDhpVRZWsfvAn4i+1c/n0gFOqyxM7ttt56P+K2LXTATwFQEhId7oMuIWgoE6t1o7TWXV+HvnLfqEq4xhhk6Y0qY7rL/fnv2+mkZm7jejwAc3cwrbjrRNgCCFallIKe+oxyjdup2zNJpwFxZg6JxByy1X4DExGMzb+E1IpRWnmQQ7Me5uovoFc8PxIzH6nNtOzcilWPLWGVeUlXP3CoFOqyxNVJdUs+/cKjqxOx2Wv6WZuCY4ibvTlhPQYKolvK3BVV1O4chmFa1YQOHy0292mf6/rsBACIs2kH1xO98HXtEAr2443xvtevXqRm5vbrHVK8is8Ur4zhayXv8GRX4wxOoSgi0cTMH4QhiC/xg8GXMrF/vwVHCveTrWrgviAAfztkSyP27Hz7XWUHM4nYlg8jnI7VQUVHJ67m+rCSl7fBwOGWenSs+WWUlBKsWO7g2++qmD21xUkJul59oUAPp55ERZLIDabe12+RfPIW/IzJVs3AmAvyAO6elxHck8zoUHdSctY5VXJrxBCeMqRX0jeu19QtS+l5invwD7Yzh6OKT7a7Try9m8ga8siKguzsIbEcPHrwzBaPfvZmbr8KNs+3kmn8Qkol6K6zE7KolTy9xcAsHtxJj3GRLRo4lmaVcb+nw6xZ85+7KXVDL17EFk7+6IzmrFFdULTOu5T7I6mdOc28hbOA8BeWIByudB0nr3/Or3GmJuSmPOfLST1uQCTpfEJs0TbeeaZZ3jwwQd58sknSU5Oxmg01tnv7+/55yfJr3Dbe58VcezRmVh7JhD99+swJXgecLLLDpBSuI6EgEHE+vXBzxwGfHbSYyorFWmHHRz4aRuHv9tFZUEF1YU1s3akL0vBYDVi8DURfWYSgd3DyPp8FdPPP8Y5l9gozHdy7e2B9B9iaepl1+utN8p47ulSIiJ0XD/Nh9vv9MXPX8e8uc3XLUO4L2ziZIwBQVRlppM99xt8uvTAGBDocT1Rof3Yvv8rqu2lmIwduxu+txkwYIDb3zebNm1q4dYI4b0O7q4i87FX0IwGwu6ehqVPN4+fsDkqy0hd9gW+4Ql0OedW/GK6YbSuP+kxyqUoSCshfV85O77YQ/HRYspzKgDI3JyF3qzH5GskuFsQZ183isM/7OLTezbSY0wE1RUO+p0XQ/8pMegNzZeMZm3PYe6t89H0Gp3GJ9Bvah+COgVS+U7vZjuHcJ9fcn+c5WXY83IpWruSkh698O/n+dP/ARdEMec/eyjM2U94XOv1HhCeGz9+PADjxtWdjE4mvBItbtfeKv70t2z8xw4i/JbJaE0Yr1NSlcPu3EUARNl6/C/xbdjuHXa+n13OgnmVZBxzAjnEjOlEzLguBHQJIWpUIrjUCW15+dY0/nRlBj99XbNs0qrFFfQdbGbihTYGjrCQ0PnUnwj/NLeSieeYefXNQAwG6erU1gx+/oT+P3v3HR1llT5w/Du9pE96T+i9d0RAAVFUsGFXbKtr2XXVdd2fa137rtjW3l0EsYJIE+lI7z3UFEJ6T2aSaff3RyRspE0gyUDyfM6Zc5KZ+973mUlm7jz3vWXUJbjKSjn46rPU5B4+reSX35IrdQ4MBfJZC5kDNGHChLqfq6ureeedd+jSpQuDBw8GYPXq1ezYsYN7773XTxEKce5z1iieeiAPXVgEUQ/dhS4ooMF1uKsrObx+HigvQfHtCU48eadwVaGD7T8c4ODyHHK31a7gHNHJRqcJ7QmItNJ2bCo6o+6YecKjrgphwRu7WfrhfgAOrC5i4dt7GHh9Msm9bST1CkOrO7P2OWNJJqZgIxO/m4AxsOlGkwnfaPR6woYOB8BxYB81hzLhNJLfuov1Z2l7d9paSHv/vxYvXtzodUry28ItcPj2J/7b5LtO+njmgqloLFXk//QrJtOptyEY/N2r9X5319hZ//2/AYjvOgpP5y6UGmrrueuhh+rKKaUoy99HdtpiyvL3YLSEEBzRg67DBxG2Nhfr2iBYW1u2Cvtxz/2PC+4F4x6i4jdSnL8Lt8vB/j02/v1kHigv0Yn96NBrIks++PCUzwPggQPHLoefH/gjBRVa/pJ5ab37jev3+FSnaByWPsX1ftfllQJg7lhV77G3SpN8qs9QVrvNli6/FL3e3ThB+llLmQP01FNP1f1855138qc//Yl//vOfx5TJyspq7tCE8LvXS9r6VG7qmyffMrB4z3oy9x9kcNcJBH2pBRynrNNpq/+hsXHNR5SXZRKXOJh4SzcCMpwALHun/uKP1WUF5G9dTNHe9Wi0OgKiUki9cByxB40EaiLQ/FqboeTMOP55F03qiTuqLwE9ZgJQtXUL1bpwfnn7AMqVht5mI+mRv6HR6xl8zd5TPg8Ah6f+kEpDeCDOKhceSwCO/7nAFJLu9Kk+T2mpT+VaK60t1Kdy5u4lx95pcKGPctV7bJWP7wOP0wMsxlDswKqtOmnZ0VrftkRa4P3Gp3JNqaW09/9r+PDhjV6nJL/ilJwVJZRn7MLWqb9Pie/xaPVHe0yrSrLZ8ct/sJfmkNxnPPFthgFQWZzF/k3fUVVyiIDQODoMvJHw+B5oflt591QfUP8rLLIDYZEdKDi8FaU8RMX3xuN2snvjVIrzdqG8DR8mcUTGzG0UrM4gdmS7065DNA3Hwdr548bo0NM73l2OBg1GXcvb67cl+eabb1i/fv0x9990003069ePTz75xA9RCXFuUx4PRWnrMIfHEGTxbfuY4znSZtdUl7Jnx/eUlaYTGd2dhKRJaDQa3NWVZP76PaUHtqC3BBLb5yIiOg9Bb6r93A3K8X1xG31ICDE33ULN4cOY4hMIHXY+aLWU/bqColkzcebmYEo4vRV9C3cWsP61NWgNMqf3bOOpdODKL8MQFXpax9tzahdlNRl8W6/mnNLC2vsj7HY7mZmZOJ31O5569OjR4Lok+W3Fsg862bHBjkYDnhoHut8aHuXx1A3/dJYXcWDWB2gNRiJ6Djvtc2m0WmI7DcdRng9KYS/NAcBorV2VuaxgP7tWfIQlKIou591FSHSHRlnAIjLu6JtCpzcSldCb4ryd2KsKTqs+5VXsfvdXNDotKVf3OuP4RONxl9k59M48AromYoyznVYd+fb92MyJaDWy1dHZzGKxsGLFCtq3r7+w2YoVKzCbG3d+vxDnuqoSJwfWl1BeUENNWSGmkNrtYZRSKK8HjVaL1+UkY/E0qnIOkDr2dsg//fPFxvVDr7cAirLSdACMpiA0Gg0uRwV7fnobT7WdpPOuxta+H1q94aT1+cIUF4cp7uhiXEH9+lM0ayaOAwdOO/nd8fkWAPo/PPiM4xONRylF1msz0Rj1hAw99baax5PzawZajZ7QQNnq6GxXUFDAbbfdxty5c4/7uMz5FT775oMiPnk5H6Vq81x94L+JHXQxldkHKN23Ga/LCap2KX9jsI12V9yHMSjstM+n0WhJ7n0ZAPayXEpzdhPbeQThiT0ozUojbc1/CbIl02no7ej0TTOvRikvh/YtISA4Dmtg1GnVodFqMIZacOSUs+PNZdi6xZJ6bW8s0S2w9/AcU7psB57yKpJfu/20Ok4qS5wUOTLoGHZ+E0TnPy1xGNSDDz7IH//4RzZs2MCgQYOA2jm/n3zyCU8++aSfoxPi7HFgfTGf3reZ6go3Wp0GxSvEDboUT3UVxXs24KwoBq0WvF40Oj2pYyYRktQZ8k++XeHJxMT3JSa+L16vm2W//IPAoFjad7qcgopiDiz4FE+Ngw6XP4A55OTrfpyJ0sUL0ej1BHTrdtp1mG21FwS2f7aFvE25dLiiE1G9ZCcHf3MeLqZi3V4SH74CQ1jDF6ZUHi8ZP+0iNDAJve70RjOerVpqe19SUsLq1asZOXIkP/zwA3l5eTz33HO8+uqrp67gOCT5bYXS06r59F/5XHG7jRv/FEFFmYd7rrCTuWAqxmAbET3OwxAQgkajRWs0EZzSFZ3xzD4gnI5yyvP2U5S5idKcNABydi2hIv8glUUZhER1oOPgW5os8QXIyVhNZVk2PYfed0ZbEwz75HoKVmeQt/IghxftIWfxXga+fgWByad3tVE0jrK1ewjokoQhvOEdERtm57Lg3XS0Gj1h5vgmiM6PWmBP8GOPPUabNm144403mDq1drX4zp0789lnnzFx4kQ/RyfE2cHt9PLNP3YS3S6Qm/7dA2uogX9fX0b2rzPQGS2EpHYnICYF5fGg1esJim+PMejM2jGP20lZWQYFedsoyN0GQGVFDts2fUFR4W6MASG0v+TuJk18nXm5lC5bStioMRhs4addT/9HBpN6UVvSFxwgb1MOC+6dw9BnhpMy2rd5paJplK/dg8aoJ2hAhwYfW7K7gJ0frqUio5QOCQOaIDo/a4Ht/aJFi5g5cyb9+/dHq9WSnJzM6NGjCQ4O5sUXX2TcuHENrlOS31ZGKcXbT+cRm2Tktr9GYTBqCAjS0X7i3biryjEEhTX6fnmFGZvZt3IKAEZLCIG2RCoK04Ha4dAdB92CLb5bk+6V53JWkbF7PtFJ/Qm2JZ9RXXqrkdgL2hN7QXtqSuys+csPrH7wB7r+6XwiVYxsdO8HrpJKKrekk3DvxQ0+tjSvmil/3UFM+0AGxV5PsOn0RgWcrZqrJ/idd97hX//6Fzk5OXTt2pXXX3+dYcNOPVXi119/Zfjw4XTr1o3Nmzf7fL6JEydKoivESSz9NJ3iww4m/acXobG10wGSR15PbL+LMAaG1s3NbSzV1aWs/XUyXo8Trc5ISGgyJUW1C03VVJcQP2AcEZ0GozM27dSEwpkzMITZCB0+4ozq0eq0RPWKIapXDF6Pl9XPL+fXp5ZSkVVOsDu5STvrxfEppShdup2gfu3QWRr2+iuvYsMLi6guqKL/UxcSNntIE0XpP83R3jd3W19VVUVUVO33MpvNRkFBAR06dKB79+6nva2hzOJvZXasc7BtjZ0/PF6b+B6h1ekxBtuaJHHzuo9OTnc6yqgoTEej09N55N10HXUf4Qk9mnyT+Gp7MW6Xg+qqItJ3zaW0cF+j1GsKszLwtSsIaR/Jpqfnsc4+jwpP8akPFI1GeRWHP1qA1mw8rfk/3zydRkCYgQe+6NPiEl/gaE9wQ24NNH36dB588EEef/xxNm3axLBhw7j44ovJzMw86XFlZWXccsstx+zf5yun08mhQ4fIzMysdxOitXNVe1j6aTpDrkskut3RoaEajQZTcHijJ76/1Y7XU9veez3OusQ3pd1o+g3+M9E9RjZ54gtQk30Ir8tJyYL5lK9fW7uOyRnS6rQMenwYna/vxrZPNrNu1esU5G1rhGhFQ5T8soXqA7nYRvdu8LEHvttO+YFiBjw3hsQxDb9qfE5o4vbeH219x44dSUurHTHaq1cv3n//fbKzs3nvvfeIjY1tcH0gV35bneXzyomI1dN/RMPnSZwuW2J3Du9ahCUkhqg2AzAF2rAERaHRNl/fizUwGktgFGVFBygrOkDWvsUsX53AsEFnvqqvKcxK/1cuJ391Orv+vow1VbMZEDCOYJ0Mg24OOZ8vonTZDpIeuQJ9UMP+njVVbnYuK+TqJzpiDTnzRVfOSs0wDGry5Mnccccd3HnnnQC8/vrrzJ8/n3fffZcXX3zxhMfdfffd3HDDDeh0OmbMmOHz+fbu3cvtt9/OypUr64d+BpveC9GS7FtTjKPczcBrEprtnGZzCJHRPXDYC0lMGYbFGkFAQHSzXiFVXi9B/QZQtnwppUtq9wd1FxXDdWdet1anpc8DA2g3viMr7t3Oji1f0r7zBOITB5155eKUKjbuJ/vtnwgb3YvAPg0fen5o0T7ihqUS1bf53hPNronb++Zu66F2zm9OTu0iuU899RQXXXQRU6ZMwWg08vnnnzfsCfxGkt8Wbouj/hBfZ5iH0uIyVh6KJSjy6DxeTyN/71819eHf3fOPxj3BSXT9+2v1fq/KOsChHz7D47AT3KkXloQUNBotvXpuwKVOvZfrrA7zfDtxB0gxPUruK2+x0bKV6If/KEOgT9PoIc/5VO6xh97h+u+KeezxIO66ZxWw6rjlLpry6HHvr8k5jPIuZWXeRWyakcLeA3853ZDPWqc7DKq8vP6CNyaT6bhbnTmdTjZs2MBjjz1W7/4xY8Yck5z+r08//ZT9+/czZcoUnnvOt7/3EZMmTUKv1/PTTz8RGxsr7zPR6sUZSuv9XhNbewXWlZFPXJej7VxNiG/1aTNzfT63Oe/o50Jf40gwAqW/3Siqe8y02rdFtFRNjU/lotbV72B2OspJW/k5lcUZBNqSCE/ogUIRYe7NGrtvX3cfjfOhvY+DKx56CPeMb9m3eS6uK3ujDzz+WhNtN5z+QqHnstG6a30qZ7/St3m354eu4NO35pEyOJorXmqHVrfnuOW+XdPvhHWUpFcSNLw7G7anAtApbadP5z4b9u/1VVO29/5o6wFuvPHGup979+5Neno6u3fvJikpiYiIiAbXB5L8tjoDrklk0Qf7mTM5jYkvdG/xXxpdFaVkffsR5qh44sZdjzH0aGNpNm9q9PNpTUbCrrqU/Lc+wrF1B9aep7/SpDi1t9+spFt3PXf8werzMcrtpiptF9VZGTjz80CrxRB++ouinPVOsyc4MbH+FhBPPfUUTz/99DHFCwsL8Xg8REfX3xc0Ojqa3Nzjf4Heu3cvjz32GMuXL0evb3gztHnzZjZs2ECnTp0afKwQrUFq1wC6Dgnmm9cP0W1IMAEhLfvrnlJe9qz+LzX2ErqM+CMhkU27KJVGoyH8okuo3LqZkoU/Ezn+qiY9X2u37fsD1JQ7GfNkP7Q630YNKqVwHsjEsXMP3soqvOWVGKKbbqG1s0ITtvfN2dY/9NBDPpedPHmyz2WPaNmfhuIY1hAD4x/vwtf/t42KwhrGPtiBhK4+dgWfg4rXL0d5vSReOQmdJaBZzmnp0hFzp/aU/DAHS7fOaHQtaM/YZlRakcnWtKm0TRxFfPSxvbkej5NVa508+UwwWq1vnTjK6+Xwpx/gOLAPfUgoWouFiHHjT9hr35plZWURHBxc9/vxrvr+r993pB0Zgvx7Ho+HG264gWeeeYYOHU5v3lWXLl0oLCw8rWOFaC1u+Ucy/7xxF09evYNr/pLAgLEtdypOWf4+KgoP0um825s88T1CZw0gbOSFFM2fQ8iQYRgjW+CaEc3AWV3Brl8/xhIYSYeBNx63zIHlOaSeF0tQtO8d3eVzF1E6cz7aACvawAACzxuAtbdckDiehrT3zdHWb9rk28Wp072AJ8lvK9R3fDyWIANzJqfx1sRVxLQPxKHJxRQegyk8Bo1Wh7MkH501kNCOfc7J5E0pRe6C7ynZ+CtodXjdLtzF+RjDIpp8cS2AsCvHkfPC61QsXkHwqOFNfr6WprBkD5t3/xcAdYJuzILiXXg8cN75vs8nK12xBMfB/cTddjfWDh0bJdaz3mn2BAcHB9drDE8kIiICnU53TM9vfn7+MT3EABUVFaxfv55NmzZx//33A+D1elFKodfr+fnnn7ngggtOes6XX36ZRx99lBdeeIHu3btjMNSft+FL3EK0dAntLTw5tTNfvpTJW3/ejy0mC2dAFaaIaEwRMegDQ6gprH3fhnTpg97afGuBNKbi7O2krfwMAJPVhqM8H1NAGFpd06/jEDJ4GGWrfqXwpxnE3npns65l0hLU2EvZtuQ/OO2laDXH/67pdJSTvbmQCx71fZGrmvQsSmctIPiiEYROGNt6/i5N2N43Z1u/ePHiBjyJhpPkt5XqckEUHc+PYNv8XA5uKGHrinIqM/fiqa4CQGsw4XU5KVy/mNRr7kVvObcaRbejipLNv83/9HrY+/azAIR07UP8ZTcBUFbuZe8BF317Ght9+LcxMZ6gC4dR8sMcjEkJmDvIvoANsXP/D3U/7z7wI3GRfdD+tjqp1+vmcMEm9qbP4/wRRlJSff8YK1u5guD+A1tP4kvTb31gNBrp27cvCxYs4Iorrqi7f8GCBYwfP/6Y8sHBwWzbVn+V1HfeeYdFixbx7bffkpqaespzjho1CuCYlSNlwSsh6otvZ+HRjzqStqGCDQtKWLpYUb5rM67yEgA0ej0oRcGK+SRedTsBiedWW6WUIm//0bUetvz877qfB175IlqdAeX1kLW1lOh2gRitjfu1V2swEDn+KnK++JiSRQuwjbqoUetv6Q7tWoDTXgpARXEGFUUZBIXXrlWjlKK88AD7N3yDOdhIx4sST1JTfRXLVqOPCCN0/EWtJ/Gladt7f7T1TUWS31ZMp9fSa1wcvcbFUfnRGJRSeByVeF0uDMFhVBccJv3bd8hd/hMJYxq+VKLX60Xrpw+dolUL0VkCMEfGYAixUbplDQBGW23vlKfawZCLs9l/0M2I88zcfkMQl11kZddeF/sOuLjoAishwWcWe9gV43AdyqHgw/8S+9if0Ie33CFnjc3pqqz72aAPQKPR4nZXcyhvPVm5q6hxlhMb2Zu3389uUL3uinKM0ae3NP45qxlWe37ooYe4+eab6devH4MHD+aDDz4gMzOTe+65B4C///3vZGdn88UXX6DVaunWrf7Qs6ioKMxm8zH3n0hT9woL0dJ07BtEx75BHIyobcs9NdW4K8sxhoXjqXaQ9f2nHJ7zFe3u/BsaXcO+Gp5o2GNzKM1LozQvDVNAOEndLmbvmikABEW0ATQopdi3bjqrv9tIWLyF8+9oQ6fhkSgvpG8sJqlnGLZE34fSHk9Apy7YRo2leMFcjLGxBHbt0QjPrHWorqq/NaTREoxSXoqyt3E4bQmVJVkEhCVw7ef9sYSefOrN//KWVWCIjjonRy6ekSZu75u7rW8qkvyKOhqNBr316NxHS1Q8YT2GULz1V9ToiT4NFz548CDTpk1j2rRp7Nu3j8svv5ybb76Ziy++GF0zfgiFdu+Pq7SImFFX4KoopWz7epTHQ8HyubjKiqjOP4zZ4eW5/wvjoykV3HJvwTF1XHaRlU/eisR2mtNBNTodEXfeRO7Lb5L3xodEP/xH9CEyHPNUysrK6n7W68x0a38NLreD9ds/wFFTSkxET5LjhhBojcZqfbtBdZtiYqnOygBOvSF7S9Ecm95fe+21FBUV8eyzz5KTk0O3bt2YM2cOycm1Pfg5OTmNuv/u8OEylUCIM6EzmdGZavfc1VsDiTr/YjKmvYMjLxtrXPIpjgaXt4a8moPkVO+l2HWYUFMscdbOxFrbo9f6nqScqaDwZMITexGVOgBrSAwBofFUlWZTUXiAHUvexhIUTWHmRobdlkrO7nJmPruDmb+rw5Zg4frXehPf5fTXPwkbOQpn7mHyvpqCdtJdWNu2P7Mn1kp43EdX9k7sOhajJZS9a7+kMGszIZHt6Dz0DkJjOhGWtK5B9RriY6hctR7l9cqV31OUb4jmbuubiiS/4qSUx423pprKzL0EJZ94qOjatWsZOHAgAFarlfHjxzNx4kS+/vprLrvsMoYNG8ZHH31Eu3btmuVqsDkqjsSrbgfAEBJG57/+C0+1g+yfplK6dS1GWxSzv4qhb08TD98XwnufVvDYP4t54K5g/v2f2uRr1nw71TUKzmAtJF1gAFF/vpu8f79N/psfEvPoA2hNzbfn4bkoODiYTm0ux+2uJj6qLwaDlf2Zv1DjrGBwzwewWk5/ZWZr566UrliK8nhaT49wM1z5Bbj33nu59957j/vYZ599dtJjn3766eOuJH0qdrudzMxMnE5nvft79JArL0I0hNZQm7CW795y0uTX7XXxa/HXOLwVANgMcbQPGUxRzSF2lCxiX/lquttGYzMlnHAOZ2PSGyx0GHRT3e89Rv8Fr9dN7t4VZGz9CXtZLm37X8vFj9Ru5ZKzu5x3b1hF3wkJrJle+yW9+JCDigLftlc6EY1GQ/TEGzn8xcfkfPExCXc/gCku/ozqbA1SelxO8eFtRKUMwBocTXnBAQqzNtOu//VEJfc97XotPbtQPn8JNQcyMbdLabyAz3bN0N77o61vbJL8tnCP2Pb7VO6ncYePuU95Fbs/XktQp1jibjWiDzhM6luvHlvO7Sb/49qNpgMHD8R25eWMunwOkMlfrg1h95pufPToGjp27IjJquWCG2O58OZYwqJrG9vXrkrBUV1CQsJgtNoT/0suWvjYCR/7X3Evn3i/sVjVngoiCCm28advz4Nvjz7W9QYPCysVOtMzeGrsAIx+/Ep2vD/bp/MC3H3+ouPen9ejB+9ct4qYX9/jiqe7cXHsQZ/qm5vzNitWrODVV1/lhRdeoHPnzj7H0lxGa6/xqVzFDYMBqKkspixvLwZTIGEJXY9b9qUFGb/9NIfsgzU8dNkeLr0tnNv+r/5w1xS9bz0TSfOrAQgsjqekpobIWbnHT6J9+xc7tzRT8tucCgoKuO2225g7d+5xH5c5v6K1GR/g276886/cftz7l/1jMcYgI0MfDCWs3XZWRB772ayUonLjRhzTKtCYzST+9VH0ISG0659OO/riyC1ny4u/sH7zDNBqiBnWhrY39SekQ+32MiW3a8kvTyPB1huz4cSf3d4dx9/D9fdCVmac8LFQlUJMxNUE6EMxHrbw2XcD6x5LfWwCJTodwb2/onzTWgDm/BDL6sILuP4231aZ7TM87bj39xw6lEV3F1I262NGfXIlVbExPtUHsGfPHl599VVGjx7N1Vdf7fNxzeni2Pt8Kpf7l0F4ahxUZuzBba/E1mPwca/ADrthA5AAHMZdnckPdywgvEMYo97RoNEe/VuMD9ng03lXLajdN1h5k8nXaAn4JZeYg22OW3Ze+ac+1XlOaYHtfVOQ5FecUE1BOe5yB0l/uxh9wLHDmJTXS9WGTZTOmY+7pBTb1RMIGjb0mLk/nQaG8OxPvdmztox9mypYOCWHuR9mozNo0Ok1OB2/ApCTs56wsLagFFX2Atq0GU1IyKmHXzWEXqMnjOPv86bR6tAA3a/9BzXlheyaMZni/b594J5KdLsgLnm0EzOf2UGn8yMB3yf633XXXezevZtrrrnmrEx+feFSTnJ2LaEkazsVBbWJv0ajZcAN/zrpcdkHa3j61v1Exhu49oFjVxNsKLO5dt51dXXJGV1BPpdofrs1pPzZ7sEHH6SkpITVq1czcuRIfvjhB/Ly8njuued49dVjO+iEECdXnFZE20vaEdbu+GtTVGdkUDxnNtX79xPQuzeRE69F+7tV1i0xwQycPIHiLYcpP1DIwa838+tdX6HRadEadXgcLgCyijcSGdQenc5IZXU+sSFdSbD1atTno9FoCDMdf32HI6N+osdPJGzoCPJmTKd8y3rCho444/PqzQYGPn0hv9z2HVvfWUNbjl0I6ETeeecdPvjgA2pqas7a5PdUlFLkVO8hc9avVOzfgfK4AbBExmGNP/H3Hpfdxc+PraD0YBnjPxqNxsftC09Eo9VhtIRQYy85o3rONS2xvW8KkvyKE1Ke2i6hmoKK4z5evnQFJT/8iLVHN6L+cDvGk/RwWoP09LownF4XhnPJ3QlsWVSMo9JDdZWHnXMGYDaHcfjwWkqK92G3186/LSlObfTk1xc6gwlreDyRnYeQv/NX1myMZ2Af8xnXO+CaRNKWFvDdk9sYqO+BSefbIhvffvst27Zt45prfLvCerZRSrGN1ZRsLiYwMhm9KRB3TSXRHYae9LiV80p5629Z2KIMPPVpG6xBZz6EzuOpHdqm1zXfnDS/a4E9wYsWLWLmzJn0798frVZLcnIyo0ePJjg4mBdffJFx48b5O0Qhzilej6Iq347yqmMSD2duLoffehN9RATRt92OtUuXEy5wpdFpCe+TQHifBJIn9KBwXQaOvEo8NW683xuJDGzLoZItFFel43Q7qHFX4FWeRk9+faHRajFFxRB23khypn9OyYpFcMeZ1xuSGkaP+wax6bVfCU7uRmSQbytoP/nkk/To0eO4K+eeK9Ltm0mrWIXFnIjJFkl1QQ7m6ATMUXEnPKb4QBk/P7qMilw7YycPJ6pL43RMe9xOdPpW1NZDi2zvm4Ikv+KEzDEh2Aa1Yd/rCyhZd5Dw89rjqCjH63DgKSundO7PGOJiibpzUoPqtQbpGTz+6GbwxZv6ABAaVtsruHvX9+TkrCcpeURjPZXTEt19JAW7VrJmg6NRkl+NRsOVz3bj1UuWkeHcRoeggac+COjatStdux5/ePC5IJ9DFJNPaGwXKgvT8bhqSO47nuiOxy465aqupCh9I89MOsCm5RUMuSSE+19IbJTEF8DprO3IMRrPYCL3OaY5FrxqblVVVURF1X6G2Gw2CgoK6NChA927d2fjxo1+jk6Ic0+na7qw/vU1zJ70I23HtcNeoUO53XjKKyhfsxqA+PvuRxfk+2enVq8lavDRq32eZbXHdoqt3aqsoGI/GzOm0yby5B2hTS2oSw8KbRE4MnybjuSLdld35dDSg+zbtczn5Ndms3H77bc3WgzNrdpTxd6KtYQYoql21VBTnE9I5z7Ej56I1lB/rROvx0353m0semoV+xZkEBwfyNX/HUtY6ukvOlavfq8bt7MKg7l1LTLaEtv7piDJrzghjVZDl2evIHfuNnJnb2HPy0fn12kMeoxJSUTc1PAtkE6mqCiNnJx1hIW182l16abgdbsoz05DZzCj1RtJz3I3Wt2B4SZ6jotlyze7aR84wG/bQzQnJzVo0FCRfwCPq5oelz2GJfjo0HNXdSVlOWmUHt5NcdZWUBDYz8RfXkvi/MtCG/U1clQXo0GL0Xhu7Vt9RlpgT3DHjh1JS0sjJSWFXr168f7775OSksJ7771HbGwr28pKiEbQ5fqu2Dra2Pnldta/sRblqd0eEK0WfVgYMXfd1aDE91Rq3FVszZoBQKDp+FORmppSCkfGQbw1DvTBobjKSvB4FDrdmbc5Go2G9td0Y+Xff6aiOp8gc9SpDzrHeZQLLx4cnnKcxQ4Sxt1MaKfedY973S6qDu2n8uAuytK24K4qx9YulD63daXnTZ0xWBovJampqh3ubLQ0TjJ9zmiB7X1TkORXnJRGqyF2XA9ix/XAXVVD5sZAtBYLWovljJISpRS7VpUx96Ns0lavR6vVERc/CIuldr5RaFibZk8MvW4nhzfMoyBtDV5Xdd39b3zoZM8BJ7P+G9coMbUbFMHa6Vm4VA1GzZlfUT7bmbGiUMR3H03mxlk4q0qwBEfiqq5g34oplOftBxTWsHjiu40mqt0gnnzl+0aPo6oqn8ysFYSGpp50YbUWqYU1cA8++CA5OTkAPPXUU1x00UV8+eWXGI3GU642KYQ4vpg+scT0icVT42b5/CTQ69EFBJzxVjGVmSUc+Goj+bvS8Sg3YdYEOsWOwe2tIcgcjcXYvFfnlFKUb15H0aL5uMvqzwkdeb6OaV/biI8/89FG0f1qV3uurC5oFcmvWVfbqZxo6cJB9w6q8w9Bp94or5ecxTMo2b4G5XZhCAoluF13bL2HMvqB7EaPw+1ykL51FlqdgeDw5p8653ctrL1vCq3sG6A4E/oAE4bwM5uLUVniYs1PBSz/Np+s3VUkdw0gOWUkVZW5ZKQvAjRYrBFERTb/BtgHl06jLGsnUV3OI7xdP6rL8jm0dhbOyhIKizyNloyHxVsAqHKXYDS2/KtU1djRoCE8uTeZG2eRv38NGq2OzI0/4rSX0WbQRELiOmG0NN0XoOrqMjZsfA+jMZBOna5ssvOI5nHjjTfW/dy7d2/S09PZvXs3SUlJRERE+DEyIc59OpMefVjYGdXhdXnIX3WQQ3N2kb86HVOYlQRbLzQaHfvzV7Bi7/votEbaRA5ppKh9V7ZhNfk/fkNQt14EX34NWouFkpVLqdy+maxMD43V7663GjDoLFTWFDZOhWe5ak8lAKHGGExB+ZTv20FgckfK9mymZNsaogaNIbhDT0zh0f/zfapxk1+lvOxa8TH2shw6DLoZnaHlX2AQDSfJrwBgSdffb/t+fLNTffsg+fcDN+GsqcBVU4nREkJu1jqK83dRVpKBBrBFdaZ7/0GERrTHsnQHSoWTadDjxUuS6ohuWwFQcFrPZYH3G5/KDb7h6KqwSnkpTd+GVm9E64LiHWtI7nM5IRckcnmHd3nkj759Efibbd8py9QM8PIOGkpNlYRGnNkXjLNB7kMn/vLidbvI/GoNFn0K69+Zxj/b2XjhjS0UZ2wmNETL8p8S6Nl1I9C08zQPHlqCRquj9+D70BsstKaNcFrDHCCr1UqfPn38HYYQfmPR+Lawzz/ijr892O89OdT3K5+7PmqHozgHS1gMpRnbKTuwlfJDaXhdNVijkkgcNhFb+77E/lI7WiMkMozSmlwSgrpi1YZAhb1efb624Q3R7dHX6n62b9sGgDEgnMoNG4kefimBY26gKCyOL59ZS1zcqZ/76PAdPp33Z1s8JdoiKjucuq33ddtAXaCP03Z83MteY7X4Vh+Qfme7Ez6Wt2AmFGspv3Mo93fYy4f/l0v6t++h0cAdz6ZwwXUlwJJ6x0wMLPLpvAfd9lMXAooP76CiKJ2u599DSNSJY22pWkN73xgk+RVNQinFhuWv4XJWAaDV6gmNaE+7LpcTEdMdo6n+h7dGoyXZ4L9tfDQaLfHdRpGbtoKc3UsAiOk4DHOgjScfbtwtcXakOVEogi2+7/93LlLKS/b86VQX5ZA68T5gDk88FM7D94SxP8NFYpye0JDGWcjqZPIL3eQe3kBK2wvRG3xv5FuMFjIH6KGHHvK57OTJk5swEiHE/8pcOp2SfUc7MK1RyUT3uoCQlG5YbMeOboqytiHKevy9V5tDWM8hOEuLKFqzEABrQiq2noOJHDSKdu0btyO2quwwMamDG7XOs1Hp5rWUrFlK5KjL0ZnM9BoRypvLepGfVY3RrCU8tnlWXc5OW0JwZNtWmfgCLaa9b2qS/IomUVGaWZf4xiUPJrHtBZjO8lX3ErtfREn2Tuwl2Wg0WkoObSe20/mNfh6zqXa4T0te6kopRc6iGZTt3kTipTdjjT0678Zq1dK9c/NtP/DVD7UrPMcm+La6dkvTUnqCN23a5FO51rCInBBni+piB/aCLABMwRGkjLoZa2Sin6M6ueD23XDbK8j5+Rt0ZisVe7dh69k0CapWazh1oXNcxe6t5M6eTmjvQdgGDq+7X6fXEJvafB3O+/a6qCzOodOQSc12zrNNS2nvm5okv6JJ7N4yHa3WQEh4KqkdLz5n9lprO+Aa0jf9SHV5Ae7fkvfGtn5L7V6zZoN/OgOeeuopsrOzeffddzEYGr9h9rpqyFk8k5Jtq4kbfQ0hHXs1+jkaIm2/C7M5FIPRt32VW5wW0hO8ePFif4cghPid7R9vpKasAEtEPEnDr8MaEe/vkHwS0qkXVelpuO0VeBxN09YX59TgrKnAZPXP9KZ8VwZZNbvpYh2KRdv4OxwopSjbtJq8+d8T1Lkn0Rdf7dfOx4P7ayc0WUNOvKdwi9dC2vum5p+9ZESLUlro5tWHsrhrxG4WfV+CUopqexFtOl1C9/53nDOJL0CALYGuF96LVm/E43I2yTk2bavBarRhMYY2Sf0n8+OPP/Lss8/y8ccf8+GHHzZ6/c7yYvZ+9i9Kd60nfsy12Hr4f7jXslUOQm3+G2Lnb0d6ghtyE0KI3/O6vWz7aANzrvuGtS8tB6Aiq4yQ5K50uurhcybxBdCZLCSOn4QpIhZvE7X1h9KqQHkJje7QJPWfjEvVsKnqFwrdh9htX9Xo9SuPh0Nff0zunG8I7tGfuPE3nPGq4Gdq3WonJmuY3zobzgbS3vtGrvyKM/buk9msml8OwOt/PcSHzx4GwGy1+TOsM6LTmyg+tJWYjsMave7gIC12ZzGHS7YRHdIJXTMOi+rfvz/du3cnNzeX/v37N2rdSimyZn0BGmh366OYQs+OVXdtoVrySir8HYb/SE+wEKIRHJiVxo6Pa+fElmeUkrcum6rDFUR2O3c7F7UGEzVFeVQc3EVQauOuO2IJrF3TIu/AaqLbDMLcjEmZHiMxhlQKXFnYDI1/JbRw2Xyq9qcRP/EOgjp0bfT6T0dIqAa3y4HyutHoWv5w8+OS9t4ncuVXnLEhY49uIh4RY2DczeH0GHg3tqhOfozqzLQfciNOexnp6xt/v9n2bWo/lLcd+pFS+6FGr/9kYmNj2bp1K/n5+Y2e/NqzD+LIzSR+9MSzJvEFuOmaYIoKdlFWku7vUPxDncZNCCF+J2Zg/Su7Ub1jGfLchcQNusxPEZ25yCFj0FkCyPy28UdChSeY0Wi0HNqziNwDKxu9/pPRaDT0DLiAUaG3kmxq3ORUeTyUrFuObeD5Z03iCzBuvAWv20l2WiueIiPtvU8k+RVnpLzYzZTJuQAMHx/KR8s7ctPDMYSGn7s9wQDm4EgASnN2k5vvbtS6b7wqiJSI2uHAFmPLG57j76FPv3f79cEEhSSRtv1b3O4af4fT7GQYlBDiTCml2PKftQBYogK4fOYNDPzHcJIubINWd+4OItQZTQQktwegLG1Lo9ZtizHRffi9AJgDGnfXCL/SalBeLxpt0+/W0BApqXriO47k0K6FVJY074WFs4W09745dz+xhF/86y831/3sctrZte4LKks19D7/T3hVAq8+XPvYstl/9VOEvss9wXRUpRT2bTvRBgWC20NYiO/J3HOFvl3tdtn3E6API6BSC5SfsJyv+/5l//3Ee+3+XnU3h0/l9EYP3monnrIqPOVV1GTkUbVxD9V7sgjo24HoP04A4M7ba3u07aVOXvxRh6XgW25+6tg9V/e7K32OcbfTtyvHLx8YW/dzdU4p9owirKkRmKOPjkbwuj3Eelayx76atOUf0892qc9xtAgyDEoI8Ztq5Vt7tmnu0SHASnkp+GU2JasOEnXxVYT2G0LahqMLGyVP2exTnW67b3u1NgVH1PE/2Jx5uThdtdNivIlBfFwyyKf6vt3Xy6dyhkO1+wHHkYL58MnbXl10lE917n3Qt218PPHVPpVTLh3K7cZTUYW3ogp3QTGObWk4du5FFxhA1MN3oAuoXTDyyaHf4fUqXv3FyoGNi3jpQzta3bGLXO2rjvbp3B97zD6Ve2nB5XU/e+x2nBlZaAOsmJLqryweV7qGQ8rD1oWvc2HSvRi0586aM41C2nufSPIrGkwpReaeX8g5uAqFly4DbyMwNMHfYTUax7YdFHz0GQBxjz2MyfRjo9bvcXkpqD5IvLX59jVWSuEuL8WRk0VNQSnmrm0xRNlQStWtzqiUwlNWietwAfYNu6j6dTOe8v/5sqLVYG6fgHK6KV+4Eds1I9EYdDgdHrb/kse81/eABobfkdpsz+uIirRctj34JcrtRWPQ0fXFawjpmYirzM6up2ZQUZmNBi02Y+tbBVKjFBrlewvXkLJCiJatIm07hYvn4MzPI3L05YT1H+rvkBqNx+Hg0L//BUDkdTdgTkkFGveKYX7xLoKsMZhNIacu3Eg8djs12Ydw7snDEB+FuUMy6rfP9SPtvddRjSuviOrt+6lYtgF3TkG9OvTREeisFpzph3Bs2Y2la3s0ZhN7N5Tz3eQM9q4vZ+LfUo6b+DYlr9NFzsuv4S4uASB03EWEjh2N8nop/mEW6bm1C7FFW9uhaYWDW6W9940kv6LB0nfN49C+xcSmDCG+7TAsLWk4D+CpqL1CqQsOwhAb0+j1712Rj9PrINbS9CtAel1OitcspWz7epzFRxs3jdGAPiIUV14RWrMJjcmIt9KOcroA0AYHEDy8F6Y2ceiCreiDA9BHhaILsFAyexWFn84l/Z5XAXjitzq7jIzisr93whbfvFsKeV0eDk1dhUavI/HGwWR+/isVuw4T3COBnf/4nprcMgbariDMGNuscQkhxLmsan8ah7/5DGtKO6IvugJrant/h9SolPPoKs+mhMZfqdpTYaegNI22CSMave7fU0pRuWUT5atXUp1+oN5jhvgo3AW1yaI20IqqrsFrr70qrDHosfTuQsjY4WiDA9EFBaILC0YfFkJNRja5z7xJ0UfT6+p6CYjvYOXhz7rSZXBokz+v36tcuRp3cQkhF4+mbO4Cqvfuh7GjKZ3zMxVLV9DZNpKkoJ6y37s4KUl+hc+UUhzat5RD+xaT2mUcCe2Gn/qgc4zyeilfuhxT21Sibr+l0T9APW4vi/6TRpgxjiBD0y4KpZTi8KxpVO7dQXDX3kRdcBnm2EScPTwUff4TKEXwmEF4HTWoGifaQCv6yDAMMeEY4iMxmI/fIxg2bjDWHm1xF5WhnG6GBGwlqWcoMe2DmvT5/C9XjZf0tGpK96TjOFRC8cp9hPVPJfPzX4m8sDNxV/bFkVVM5e4cOj97BWHvtOLEV4ZBCSEayH5wLznfTyGgTQfir7vjrJvf2RhKlyxGa7EQe/cfMUY3fkd34TfL0Gi0xEX2bvS6f69i3RoKvv8aS/sORF51LebkFLQ9gyj7cQk1ezMJPL8vaDR47Q40RiP68BAMUeEYEqLR6CzHrdOUHE/85MdxZeeiXB68VXZuHbqDzoND0GqbJ7lUSpF7wIFj9x60ZhPF381EFxZKxYpVGOJiibjxWgAq16wjaNgQkjN7NUtcZy1p730iya/w2XezKzm4czZxbc4jvu35/g6nSTh278GVm0f0A/egC2rcZM7rUSx4bRcF+ysYaBvbpD2T7qpKDv80jar9u4ifcAvBXXrVPeYJcBB1ry9ziT0nfMSUGIUpsXZ+0oDOJWcYbcMc2l/N07enk3/IBeyvu79k3UG0ZgPt/3oJGp2W6pwyAIyRzZeUn40auqhFa10AQwhRq6jAw6GpH2COSyJ2wo0tMvH12O1UrF9HyJChmOIb/6pv5fo9lMxeTfvE0RgNgY1e/xHK46Fk4c+ULP6FoAGDiLpyYt1jHm01oRMuOHUdrhM/pg8NRh8aXPd716HZZxRvQzirPbx3/052rigB1tfd7ykpBSD2L/ejt4XhdTjwVtnRh4ZAZrOFd1aS9t43kvwKn5SUenjlrRKCbam07Xb5qQ84RxVN+xpdWCjmdm1Puw6Py8vyj/eRvr4IrRaCYyy4HB4ObS2h9LCDMY90QTvNt8UgTkd1fg5ZX3+EcjlJnHgnge26NNm5fFVy2MGW/U569DI0KOmvKHXz1Vv57N5op21XCzc/Es0X/8pDq9XwyjdtmFpyPrufmUl1dm0CHndVPyr35ZM1ZSUlaw8Q2CEGa0oE4NvCHy2S9AQLIXzk8Sjee6MCNBrir7sdnSXA3yE1iaJZP6Kqqwka5NsCVydSvnIHZYs347XXYIgOA6VwHiqkev9hAnq3I9nYdHOkvdXV5H75OY59e7CNGkvoyAub7Fy+sle4ydxRiblbCHqj750mXq9i0efZbFlYhCVYz6X3J3NwSzm7V5dy52udmVs6gdJZc6nauBkAY3IiGpORoq+/p3L9RtDrsPbsDludJz9RSyftvU8k+RWn5HYrhlyaRX6hh5RuF/s7nCahXG7QajDExuApKwevF3Qn/+DOznHz1YwKdu918s2sCi4cZiX1L0mkLc5j8dtpRLYJpKKwhqoSJ+YgA+2HRdPr8gQSeoSxaloTPAevl+I1Syj8dQGGUBuJtzyAITi08U/UQMs+O8jsf6UBMPW7cPoNMPp0nMeteO3hQ+xYX0ViWxPzphVTUebhcHoNfYcH0alPAAEHIun64tVU7ssj7dkfKViwg0PTVmNNCqfNfRcSNaYbWn3Lu2rRENITLITw1QtPlPHtl1VEjLq8RSa+yutFuV0YY2Jq23iP95THuGs8bJ2VRUlWFTvmHUJv1BL8p2hAw+FXv8EQGYqrqByvvQZtgAljnA3bFUMJGtgJzatNs+hS1e6dFM76AW9VFbF33I21XdOvIXIqezeU8/pdO6mu8jD8bicX3O/b7hcAi77I5tuXD9D1/DC2LioibXUpQ66KJjLRTL+LI/llQTjhN07E0r0rZT8vxJmdw+EXX62dvnX+UIKGDkYfFgo035Xps5G0976R5Fec0q9rHezZ72LZzAQe/zjF3+E0OndZGYee+CfaACuRd0wi7813yP/oM0LHjsaUnHRM+Q1bqpk5r4r3Pi+lqMSLTgdKwcy5Vai5C+vKFRyoXTir/4Od6XdNcpM+B09NNdnffUZVxl7C+p5H5PCL0Zl820KgKRUcrGTua3swB+mprnCzb4+LPv0Mp5wvVFPt5aV7M9i0opLH3k6mON/Fni0Orrwrgn/elYE54OgXCnNsKBpd7e81+eXEXNaLNvddWHdfqyc9wUIIHzhrFD99b+fuPwexOHiEv8NpdEopcj/+CMeeNGLv/xO6gAByP/mIsDEXEdjr2Hm5VUU1bPz2IHuX5nF4ewkGiw6Xw4NWp6H44ffryrnySwEwxtqIf/TaJn8OxT/PpWTRAiztOxAx6S6Mkb5tkdSU3C4vn/9jH4GheqqrPGRtLsFR7sISbDjlsXPey+TH19MZeXMcF9wczxNj1nH+dbGUFTgxWY52XmuNRgL79absl8XgdqO3xRJ9zx3ogppuWPk5R9p7n0jyK06pylH77vjbPwvZlTCH0CtGojGc+/864ZtrEzCXQ88hwFtlh0W7AHDs2IW2wE6nS//EuH9dAkBRdRYHKzZQWJ1Vrx7Pb1NjdRjpaRpGmbeQw+79OFRt8rvvJQs1r4bVO+bHon+h0+nQ60/+OrZ75bWTPq6UovrAPoJmPIW+2MMv38Qxcmgu8KkvL8Fx6/tudiXrN9ewdmM1O/fUDiHq2dXEi4+H06fHsQn19QdPPKdo5eQFWKIC6f/4CJbcN4sn/17OU/9XhVIeuvS4gYjILiil0OnqN5BpSz8iz+mgd+BYNvw9gV32lVi0Fbx3g41Sx36yvxzKU/+tYlPVv8jKyiJhVAJpff/CgQMHuOiii9Bqf5f45pzWy9EiSE+wEOKIGN2J3+CVyovbpVj4k4PDEV8SOn4MelvYCcsfoTH7tpfqgsrPfY6zsUVuUiilKCypbdOcP63AU16OB8j/cgpJri7o9EaW/DqUansJ6dt/oiR/Dx730SkzLkdtY+/1KDq1uRyFIrdgC2UVmWg0Osx7NCS8+rs2vaISNBq0mlN3xu555OTTrVyFhRTN+hH7oh3c9NdYJvzBgkYz78TPWVdx0vp2b61m5UI7q7ebOLyzDJfDTVCUmWF3daDr2LjjTlF6e//xFzotnLeZnIMOOr11B4X3f8yBNYW8dN7PoLxEdhpK0sAJeD0utHpjvXqrFy9ne/oy2sSej37HCL7/415gHbmLB7D94AzaxY/kv1d1In3HI/SOmUB0YHvaGK+kJCaLcH0y+k+qgKq6+twZWccG14pIe++bcz+DEU1u1DArf74rlJx8N6tnr8Cx8yCR912DIfLUjeK5wGAJJHXEzWSu/Ja87Yvr7o/rUzvEWynFnrJfOVixod5xGrQovOgw4MGFzRxHhC6eCOJJMXQj3bUdL17i9Ec3pFdKsa1mOWazmVdffZWHHnrotOO270mjaO5POA9n06+niRmfx9GhrW9Dio+npNTDvY/l8/XMSlIS9fToYuK+20PQajRMn1lB/4uySE3SU1jspcrupX8vMwu/PfFCIUopcldnEdEzhoCYIKwBUdir8lGq9gvEzq1T68pqdUY0gN5gJSAgimLnPrpZzyfCULt/tNNbg8NbwV7HOlLNPQnRR7CpchMAmZmZJCQk0LFjRzp27Hjaz7/Fkp5gIYQPAgO0PPdMCBs2Ojnwy15ydqYRcccNWDq1O/XB5wCNRkObQdeye/GHlB7eVXd/ZJsB6PS1bWfh4W3sXvvFcY7VolTtEGmDKZDoiG4YDFYSYgaQV7iNotK9tE2qP+f2UM4adh+YhS0ghf5tbjztuF1FRRT9OBP7zp3oAgP527upDBh9+vsGezyKrz8q5YN/FRMUqiU4OZTu4+IxBxnI2lTMd49uYP6/tuP1KBxlLgLCjNz47iBiOp34nOUb9qMPsaIPshAypANlK/fAb69Xwe5fKdyzGuX1oNHp0Wh0aPUGzKHR1BTmEBPWjbaxI9BoNDjddgC2p88gOCCO5KjB5JXW/q3Ka/KJDmyPUWchOtD/w7zPStLe+0SSX3FKRqOGyc9GArD8gwvIf+1LDv1lMtZ+nbF0b0dAvy7oQs7tYSehyd0ozdxOyYHahCo0pQcajYa98z8gvdBOUU393sRgQxT9o65ke9FCdFodPcIvAsD728brOo2Otsaex5zHoSrI9aQDUF19eoswKaUoX7WCwpk/YG7Tltjb72L1c4sbtJCU16vYvL2GRSsc7NrrpLzCy5KVdlwuRWyUjvQsN3+6M5Q//6G2gyM6Ssfdj+Sj1Wr4x19szF1YxZKVDvZnuOAEnf4ajYa+j5zH2ueXsuiPM0lJvYzq6hIO7puPyRxKTGwfzJYwNBotLldtg+d0VlJRnk24PoFY49EvXB2tA9A5dIToI0kw1s4j6hVwIe9ueJw2bdqc1uvYmrTW3l0hRMPcdmsAt90awMqpd1Lw/hfkv/YBxtQkrL26YuneGWN8428J1JzMQRHEdRnJwbXfAmCwBBPdcSh7V0zB63FSlrPnmGN6j3yIypIsDu6czcCLn0aj0WAoqAFq27mYyB7ERPY45riM7BUAeLwnWU75FKozMsj96EO0ZjMRV11FYN9+DBj9VYPqOJzlYuNKB2nbaqgs97Jnew2H0l306G9my9pqoroZGPVg7cKYuWll7FmaR2VBDcPv6YDXC8s/2MPBtYUnTX5jbx7O3r9/yY673iP6igFEXzSSPfNrh4ZHdRmGOTgSndGCu6YKpbx4XTXYiw+jMQTRLm5k3feX2PDu2GuKUMpLm9jhaLU6Ym3dsHkjMOlb3hz0piDt/alJ8isaxNw+iYTJD1OxdANVv26h6NNZFE+ZQ/BFgwm9fDhaq//nmZ6O4v0b6hJfrd5EafpWStO31j2eGtSXhIBupJWtoNpdQa+ISzBoTfSOvKRB58l3H6r7+eabb/b5uMotmyiePxdPVSUagwFPRQUh5w0nfNxlaLRaNJoldWULCt18+1MlifF6xo0KQKPR4HYr1mysprLKy6r11Uz9voL96S6sFg3dOhlBA107mmiXoufTr2qHSiUl1A5F/mRaGXc/kg/AA3eE4HbD2k3VXDomgK4djZB+4riTx3bA1jWaNc8sYtf2r+g/+CGSU0ee8vma1u+r97tZG0C3gPrba2k0Wkl8faFU7a0h5YUQrZouOJDoh+/BsX03lUtXUzZnIaU/zMXapzuh4y/CEOP/eaano6aqtC7xBXA5ytk+9+j0oqCwJLoMup1DexdTlLOD1G6XEhASS0BILNEpA3w+T5W9AEdNbWd4aqTvK0o7c3Mp+Ho6rvx8NEYjnooKTMnJxNx+BzqrtV5Zt0uxal4pjkoPI660YTRpUUpxaF8Nxfkuyg6UsXhOJVvXVaPVQkp7I9ZADUGhWkZPCGT9CgcAtqTapDJ/bzmfTfoVgISeYcR0CmHB5J0Ex1joMS7hpHFbkiLo+uE95Ez7ldzpKzEMjKLfba+e8vnatpbX+12r0dE+/thVqy2G4GPuE8ch7b1PJPkVDaY1Gwm5aDAhFw3GU2GnbM4Kyuetwr5uJ3Ev3IfWdPpDb/0lKLY9wfGd0JksRHYeyp7Z/6l7rHf4pURba+fi9Im49LTP4VYu9rlqE+wRI0aQmJjo03HOgnzypk3B2rEzQQMHoWpqMKe0wdrh2CG+DoeXC6/OZvc+Jx4P9OhiJDHewMp1DkpKa4cghYZouWxMAG+9EMn6LdUsW1XNL8vsBAVqSc+s7aG2hWlZvtrBr2vtvPZ+WV39cxfZmb/Yzl03BTP5mcgTXm32uj3krMwiY/5eSnYXUJVTgU5vxmiUntvmJnOAhBCnQ6PVYu3RBWuPLiiPh6rVGymd/Qs5z79B9CN/xJR88oTobGQwBxLVbjDVFQXEdhlJ5sYfcZTlARDT6XzadhyHRqMltdulpHY7/fY+/dAyADRoiAr2bYiuUorcTz5Go9cTesGFeJ1ODOE2Anv3QXOc9UE+/uchfp5au6Xi9Ddz6dArgMw0B7mZR7f76T/Mwj8mR6EUrFlmZ9ncKrxKUVLoobTIg04PlYU1rJ6yn0Vv7q6b1xwcbWb6g+tI7hfO5c/0JCD8xPO6q/bkUPzLViq2ZVCTXQyAOTjC59dKNB5p730jya84I7ogK7ZrxxA4tCfZf3+bikXrCbl4iL/DajBTkI32F91V93uP65/GU+OgprKYyJ2+LeZxKnZvBV5qG5ZHHnnE5+M8lZWgFOEXX1q7PcP/UErVbsv0m4Ur7OxIc7J0RjzDJ2RT+7DixiuDiI7Sc/lFAbRJMmC11i6+cckNhwH44j/RTLw8CINBw94DTt7+pIxvf6okO8eNwQAuF9x4VRChIVq27dLx3r9OvE+x1+1l6YNzKNh4mNAOESRc0IbglDD0y4ZgkOS3+ckcICHEGdLodAQO7Y+1f09yX3iLsh9/JuqB2/0dVoNpdXpSB1xV93vQRX/G5SinuqKAAFsSGm/j7BKQU1Db0Z1o64PGh8WujvBUVBB28cWEnn/swlLK40Hz2xaMNQ4vv3xVxE1/jWXD4nIy91RT4/DQbVAgAy/S02d4EAN7ebH+tjPCE3/MZem8Km59IIyJd4QQFKKjoszDT9Mr+P77CrbPy6777I/uGEz7YdHs/DmHy57uiS3pxNPacqatIOe/yzCEBxIyoD2Rl/QhoEsigRs7+/ycRSOS9t4nkvyKRmFMiMbapyOVv24+J5Pf3zNYgjBYgjCHRqHdldkodZq1VgwYseliueQS34ZLK6XQBdUO98l6/V+EnHc+lrbtqNq+jZpDmbiKS1BuFw/uD+apR2ysXl9NcJAGr7f2Ey01ycDN1wRz18N5lJZ5OZjp4t5JIazeWE1ZmZcAq4Y/3BzCjVcdHVLUvo2R15+L5PXnIvnzP/KZv7iKVbOTCAvV8Y8XC9HrTj63eO832ynYdJjzJ19CzKCjV7fz18qwJSGEOJdpjUaCRg6h+KuZeCoqz/ltZnR6I7qgCMxBv12ptDdONmALbYujupS20cN8P0gpNEYjxT/+SE1GBoF9+1GTkYFjzx5cxcV4qyqxdOxIVlcNlWVuvF5o283KlH/Vbmdw2+PxTP5zBpl7qtm0rJzkNyLYvbUGe6WX9H1OUjsaueMhW93pgkJ0XP+HUKJu6MqepblMu38tf/jmfGI6hJCxoei3mE4crn1/HjlfLid64hDibj6//vaCGxvyagnRvCT5FY0mYFB3Ct6ajiuv2N+hnJWMGjPDrRPRarR1w4VramrYu3cv3bp1q1e2sLCQ119/nYw33sBTVYXGYEAfZqN87WrKli9Fb7Nh7dCJoD7heGtq+PSrBUz9vpyiktqrwCOvPMwVFwfw4eRoOg3NoE93ExeNDOBv/yzkk6nl6PWg12u4dHQA//dn2zGxHtG7u5n/fFxGWYWXsFAdbVIMZB12s3l7Db26HXtF3J5XyY6P1tHuqq71El/hPxpv7a0h5YUQ4kSsvbtTPG0G9k3bCTrf9/msrUmvLregQYu+onZhS6/yUFVdSJCl/qgppbwcKtlCZtF6Kh+tneerDQqiJjOLqi1b0JhMBHTthrVLF7RmM2UrlvPQpUV4f9ti8Zlb9xMeY+BfMzrwzv9lUV7i5k//TuLjZ7OZNPYQGg3oDdC+i4kHnznxUOS4rqEA5O4qJ7ZTKKHxVjRaSFuay5CUY1f7Vl5F5n/mYk4MJ+6mYfUTX+E30t77RpJf0SA/DH33hI85ensZ87mG4dvf58KR4T7XuXDx3xsjtAZb//HpbzN0pjo9/Rruqkr2//tJANr/30toDUfnSmd98S6OrHTuusVEh3YhTPumikPZBaxYF0VFBSQn6dBqM4Haq9K7cyL498uVfPeNo66OARfoeOPrAkrKPDz1SgAJiXra9bfxx1+vxNwhBY1exzqg37rjx9juTzvZbl8OwLCheroHnI9HuQnUzmLImEL6BY4lUBeGt6p2wQqlFFs8y9EqPQkzOlEys/5CFoudf2ukV080iAyDEkL8Jlzr29STD0d/ctLHn5hjJXfdT7z/zA5eGHD2D38Omra6UevTR/o2pzX9D7Xzfff/53lcpUUk3vRHAlLa1z1etHIhBdvnENixG9deH8qBjWWs+zGPP33ZmyBbe8JiTRhMOqAGqMHl7MaKLzL4/tV0jBYtToeXNn1DyCoysn5RObc+347ul8XwRJ8oXv22K+YObdBaLVQBz+cBC46NMXSjluLdtV8EZr+wl8ycWwAI71rDgld/Zc+m3oR16AtA9Fc7Acis3ondcZgBgZcSdmvRMXWqGt/22/U6HKcuBMz3fuNTuVZP2nufSPIrGo3FquXK6wP4bmoV/ft4GzTPpTVSLidotegsVlAK5XFTtT+N6sOHsB/cS9zVt/DyPxcBMPoCM33Py+Xhx8q4/55Aioo9/HeaneISD48+GEx8Fx2vTA7h+ZeDMRg0eDyKj96v4j9vVHHp5WYSEmvf6r16G7GUtD1lbDX7M1lZ8QNObzVJxi60NfcGQKfR0zdgLOsq57DVvpTBgZfXHZOt9lOgsumpOw+D5txb9KylkgUwhBCN7YrbbDx2SxZpW05vy77WRmex4iotQquv3UXBnnUQZ1EBRb8uJKTXQGIvvZbzrp3FkGviOLSrkq+eTGP8I23xehUbZueTsbWcARNi6Dcumov/kMCY2+PR6mq3Wtq2tJj/3LMLW6yJAZfWbksZHm/G2qvrKePylFdy4KevqDi0h+CkzsQOGlf3WPyQ8Xhq7GQu/oqA2FSMQbWjxCo9JaQ51pBo7ESY4dze+qqlkfbeN5L8ikZ1wUUWvvigkvSMJaSmXODvcBqsoqKCn3/+mSVLlrB06VIqKipo164d77zzDu3btz91BQ1gCLXR/rEX8DqdlG1eR/m2DVQfygCtjrCB5xPYpSdQm/wmJep59w0bf/tHKT/Oqe0pTYjXUV2tuPO+YuYuru2FNhg07Nju4pkny9m0wcWtt1n588PHn5PlLi2ncsk63PlF6CNtWHp3xpQSjyungLx/fUKwJph+QZdg1QXVO86ktdDNeh5rKmezsmIG4URjVxUUqsPEa9sSpZXhzmcV2fpACNHIOvexEB6t5+NX8gkJO/c6u5VSlFBQd6vGjh4jbehMlCa+0c+XPOlPeN0uqg6kUfLDFMp31E6KtSa3JWrU0U5krVbDLa90Ycrfd/H+H7cBYLLqCIk28fkjO2nTO4S4BB06vYbyQicz38xk2fRcupwXynX/1wajWXfMub1OF1VrNlKz9yC64EBM7VKx9OgMHi/5b32Ct6iUNpfeTXBi/R0kNFotCcOuojJ7P3u+f5Pg5M4UVpWS60rHog2io1WGvJ91pL33iSS/olH17GvitnuD+PSdxcTF9sNkOncWOVqyZAk33XQT2dnZtG3bluHDhxMcHMzrr7/OW2+9xZtvvtlo53JXlFGxezsanZa8WbXDeSxJbYi//g6sqe3rDYE+4uoJVi6/xML2nS6qaxQR4VouGp9P+3a1b+MN651MvOLofOtxl5n5x9PHvv6u/GLKfviFqjVb0eh1GBKicWxJo2zGQkKvvgjHlt3oQoPpZx+L/gRXcEP10fQNGMNh5z5yXRmYNVbaa3uRqG3cDgJx5qQnWAjR2CxWLY+9Fstfb8iic69tRMb29HdIPqtWdrazllIKMWAklAhCsHGYdPaxnSgaL/n1upxU7N6Gp9pOydrluEoKMUXGEHnBpYT2HYLOZD7mmIROgfzt+34UpDsozqkmvlMgH963jaAII9YQPZUlTp6dsImS3KNbGj34Uddjth701jgpm7uIyqWr8dodGJMTqN57kPKfl2Hu1hFjYhzO7Bw6TPgz1sjjb1ulM5ppe9k9FO74lYqsNBzuahKNHUkyd0WnkRTibCPtvW/kP1c0ukl3B/H5e06yDq2kXdux/g7HZzfddBM5OTm8/vrrREZGsnPnTqZNmwbA6NGjG+08ubm57J/8TL37gnv2J3bC9ac81mjU0KdXbUJ60fh8SksVCfF63nq9kh++s9crO3tWNbfd6aR3n/oJbNH703EXlhI28SICh/VDG2BBeTyUzVxE6bfzAYi4/0b0U04+dDnCkECEIQFvlf2k5YSfyRwgIUQT6DkogN5DrezdtoSImB4n3Pf9bLOfnZRSSDIdCCKMGhwUcBgXTmJIarTzKKXInPIO1dlHd4zQWQNJvfvRUx6r0WiISrUSlWrll48zObi5nE5Dwvj5gwzsRTX1El+AhZ8fZtSk+kl7+fwllP+8lKDzBxF0wVAMUbUjxBzbdlHw3hSqt6cROGzACRPfI8xhUSScdwVwdM6vOEtJe+8TSX5FowsO0ZIQP4isQyuJjx+IxRzm75B8csEFF/Df//6XBx98EIDw8HAuueQSpk6dysCBA8+4/pkzZ9KtWzcmT55ce4dWB14P4cNGEzZ4OB57FXlzv8fjsBM5+jLM0XEnrW9APyMH0928/3ElAJ99GUaHDnqiY3TMm1PNfXeXkpHuoXefo8csW1JDzb5cIh+8BWvvo/vwaXQ6Qq8cjdZqpmrVFizd2gGHzvg5N5eSkhKmTJnCNddcQ0yMzEH6X9ITLIRoKtffG86jN2ZRkLOFqLhe/g7HJzYiySGdDPYAoEVLKBF0ZyBRnDwR9EW5qxCXqsGjXFQXHE18gzr3JHLExSjlpWj5Air378I2cATBXXqdtL6wGBO2ODMZ2yvYvbKEMXfE8895fYlOMWMv9/DggNVk7Kysd0xliYuKRRsIGjYQ23Xj6z1m6d6ZqAfvpPjLHwgY3A8qzvgpNxuv18vnn39Ojx496Nu3r7/DOetIe+8bSX5Fk0hOGkFu7ib27v2JHt1v9nc4Pvnkk0+46667SElJwWazYbVaG60nOyMjgwkTJhAZGcn06dOZvn0fltR2HPr8HYqWL8BTU40hOISK7ZvQWQLI+uxt2j74xEnr/OcTofzziVBmz3Nw0x1FzJ9bzbDzQwDYtLG2V/i8849evd200cnjj5Vh6pSKpVen49YZPHYYwWMbsC/hWWLhwoX86U9/YvXq1Xz55Zf+DufsInOAhBBNpNfgACJiurN/1yxskR3RGyz+DumUYjXJBKoQtGgxYUGHvtHaeqUUK0u+BeA827XYBo0guOdA0t9/mYpdW3BXlBMxfCyFy2pHWR3+/gt0lgACUk88ZajvuGj6joumotjJ4+f9ypofC7jyoRQ0Gg0ZO2qT3pTuR9fmKCtw8vGje1BeRci4Ucet09w+lbinf9vxYlNjPPPmkZGRwe23305gYCDl5eXnzGiDZiPtvU8k+RUN0t3oW8NW2TmEKDWarF+/ozhVi1Z/9q/+q9frGTasaRK/Rx55BICCggKGDh1KzMCxlO/ZWvd46drlhPcfAUBwh56UbFlJyaJfmDrp2P31jjEUjIYNTJtSQdau+zCbQsgv2glMY3CfEpLjz6NN4gWs2PAvdDobO5f9Qps2bU5d7y2n8UT9ZPz48TzxxBNcf/2ph463NtITLIRoqCFm36ezhF5+E4UfvEBWcCZB7budsNwY4w0+1fezc6rP5/bVgkbeKme07trj3p+tDtT9XFWSQ/TICXidNXX3OQ4dpGzjKrRGE+bYRGoK8yhcPAdr3L18m93neFXWEz22jNy52/n3Ax0ITeqGy1EBbOer5w7w89QwkgaMJ2vdj5RkOujh6U/UY2mnrFNj9P37mcfHrYka+/U+IjU1lU8++YSIiAhJfI9D2nvfnFvL84lzit5Uu5+gy34OjalpRBs3buSPf/wjb775Jr169QLgP//5D8bfGhpLbBJoj74Fi9YtAaBky0oACtcuYu+60lOeRylVN7Tc63UDEGHrSGLMILzKzcFDS0g7+BM1zgo6tbnMt8T3HGMwGHj22Wfp3LnzqQu3Nuo0bkII4SOtubZT3FVZfoqSLZNbudjt3UiadxNWVXsFNpRIbEQDoDEYMccenUtcvmszXmcN9ox9eKoqqM7PpvDX42zAexzBnWJrz1lT2zlhsASRNOgqAIoPbCBr/SxKMrYS1WkoUdrGX7X6bHDbbbdx2WWX+TuMs5O09z6R5Fc0mcr8g+gtgRis586Kz43prbfe4r333uPPf/4zRUVFLFiwgHvvvbfucUNQKMlX3nncY/WBta/Z+p/yTnoOpRTfPLePsopMurS7AqslHACtRkfHNuOItNUmg4fzN9I26ULCglMb46mJc8iRnuCG3IQQwleOwxkAWKJbZrJ1KsXkcYh9ZLGXXaynr2YkvTTn1V2Z1Gg0JF5zxwmO1qA8XorXLkV5vCc9T8Hyvex54xfC2/YlvF2/uvujOg0hoX/tdknF+zcQHN+R2O4XNspzE+cWae99I8OeRZMJju9I4c6V7PruFcI7DsTWri/GwHNj8avGkJp6NNGMj49n1Khj59647bXzdQwh4YR27UdV1j7QaNDqDVRWlrNieg7XP9PxmOOOWD7tMEu/zKZTm8uJizp2yFTPTr4NMxNCCCFOhzk6AX1gMBlfv09ot36EdO2HOSax1QxLNXJ0u6IAggnTRJ60fPToK6g6uAePswad0Ujlvp0ot5fynYcJ6X78BbeqMorY9fxPRA7vQFLidcfsqxzTdTgxXYef+ZMRohWQ5Fc0mZCkLnS64i/kbl5IzsYFHF43l4jOg0kccgUa7bEbsbc0//jHPxg2bBgdO3YkLu74KzcHJLUj/pIbAEX2nGn1HtPoDQwaH37C+l1OL7PfSmfwVTEE5PRvzNBFS+JVtbeGlBdCCB8ZAoNpe8ejFK76hbIdGyjesAJLfAoJEyZhCGz5I79CNREM4iK8eAnWHL+DX2e2EjvuOoy2SDL++1b9xywBaPQGgruceIeH9M9+xRQZRKe/jsXxtQzaFCcg7b1P5B0kmpTFFkfqBTfT46ZniOt/CYW7VlKRvdffYTULrVbLyJEjT5j4Qu3Q59Cu/SjZuua3ezTog0Ix2qJAq+XSP514mPKOJUVUlri48LbERo5ctCgyB0gI0cR0ZivRIy+n/b1PkXj1nVTnZVO0drG/w2o2gZqQEya+8Nt2gj0GYM/YV/u7wYjOGog5NhGPo4qYMVeg0R3/K7mrzE7Rqv3ET+iN1ijXrMRJSHvvE0l+RaOrrDp23orOaCayy1AAqgoyUa10efUTOTL82RQRTXCHHjiL84kbdRWh0aYTHrNsWjZtegcT2y6gucIU5yANDZwD5O+AhRDnBLvdi/d3V440Wi1BbbtgjoqjOi8b5XH7Kbqzk8dRBYByObH1PY+awjyCu/YlqEP3Ex6TM287Gq2W6FGyoKM4OWnvfSPJrzhjSikcDi9ffF1OROf9hLTbz8YPHyJz+de4HEc3XtcZzYR3GEDOhnns+vYVSg5s9l/QZ5n4S64nZuR4PM4aijcsI3LIRYR27XfC8o5KN3vXljJgfHQzRinOSUf2/WvI7TS88847pKamYjab6du3L8uXLz9h2e+//57Ro0cTGRlJcHAwgwcPZv78+af7DIUQzcTrVSz9tZpLJ+YR1e4QwQlZZHz1Lo6crHrlwvoMxZ61nz1vP0P+inkoj8dPEZ9dwoeMIvL8iwnq3IuC5fOwxqcQe/E1Jz2maOV+bANSMIRYmylKcc5qhva+JbT1Mn5CnJGCQjcx3Q8e97HC3avxVG3lujcGENkuCHuJE+MfIsnYMIi1Uw+yd+EXdDt/C/CX5g36LBC2t/4XgTASICSBvIpZpHS9hISokbDXw+vfXH7c4yt3bcfrWcGq4qvY8E0Ue35tfa+h8E1z7Ps3ffp0HnzwQd555x2GDh3K+++/z8UXX8zOnTtJSko6pvyyZcsYPXo0L7zwAqGhoXz66adcdtllrFmzht69ezc8ACFEo7Jojh11NPa6bBYsPXb/36qMvRz84jXaPn4FYYM74Kl2EtM/Btuld1IwdxP5P/2CV5dFYsCQFrMI1gLPdJ/KDR/3yu/uMREbOILtuz4lNLwd3TvfjmanBnBR7Dh2tWxPtZ2K3XnEDLuc4p9qH4+bscOnc3t87HD4ueoLn8qJs19Tt/ctpa2X5FecEY1GQ5cORnbucZKSqOf/HrRx2ZgAbvncwoJ/76Qky867Vy5Bo9OgPAqdQUv3SxO4+LFuWEMNLHh1J57HPeh0LX8BrONx1lRSUZROeFw3AHQGMx6385THOQ7uRR9mwxgZ1dQhinNdQ+f1nEbyO3nyZO644w7uvLN2667XX3+d+fPn8+677/Liiy8eU/7111+v9/sLL7zAzJkzmTVrliS/QpylOrc31iW/Tz1i47brgsnMdnP++EMA7H/+B9Bq6hbRCeyeSMItw7F2iOXgv2ZRFtSeUH3rbLOU10NB7jbCo7ug0xnR6c24HMWn7AyoyklHeTwEpXRqpkjFOa2J2/uW0tZL8ivOSES4jm1Lk4+5f+ANbcjdVca22dkAKE/tO8zj8rJ5RiZbZmTWjbYoLCwkOrr1Dd9Vysvauc8AMGjcP9HpTYCGwsNbSew4iqqyw6S/9iVai5W4G25HHxxSd6xGb2i1CxWIhtEohaYBQ5uOlC0vL693v8lkwmQ69mqQ0+lkw4YNPPbYY/XuHzNmDCtXrvTpnF6vl4qKCmw2m89xCiGa12v/jOS1f9bfxicx3kDi3aPI/nwp3mpXvdVjK7dlsfuvUzBG1q74bPeUtdrkN23bt+Rnb6Rz7xuJjO0BGqgoO4SjqhCDMZDt6z/FsaKc+AuvISj56PaGWt1vX9OlvRc+aMr2viW19TLnVzQJnUHLlS/15c/zRjH64S6Yg/ToTb/9u/02zcAaZqTz6FgiIiL8G6yfuF0OAIJsyegNZjQaDZ3634yjIp/C7K3s2/wdruIiarKzqM4+Op9Keb1Upe3EFHfsECkhjuE9jRuQmJhISEhI3e14vbpQ23nl8XiO6cCKjo4mNzfXpxBfffVVqqqqmDhxYoOfnhDCv2Im9KfnlPtp87fLMSdF1F79/R+u0ioCOsURqo/xU4T+Z6+o/Sy0RdVewW3b+VJMpiAy9y0ic/8iykvScZUXU35ge73jyg/uRGe2Ygg+8UrSQtRpwva+JbX1cuVXNKnQeCtDJrWjy5g4vrhjJVUlTnR6DY4yF/YSJ7sW5ODxtM5hzwZjAIMvfR6d3giAx+OisrQ2yXVU5lNVll1XNqBDZ8o3r8dxcB/K7caZn0vU5Vf7JW5xbjndnuCsrCyCg4/u0Xm8q771jvvd8D2llE/z+6ZNm8bTTz/NzJkziYpqnVeFhDjX6QPMhI/oStiQjux/aSala/ZisAXiKqxAuTxU7T6MPbAMqy7I36H6Ra/B96LR6tBotCilKCtOx+utXQk7//DmunLhPYbiyMuicPMKDIHBFG1aRtTA0UevAAtxEs3R3reEtl7eTaJZhMZZue2L8/jltZ1s/ekQwdFm2g+PpsPwGIxGo7/D85sjiS9AedEB0nfMBsAW3ZmstIWYYuOoycnGVVJE3g9fgdcLGg2W1HaYk068B7AQdU5zDlBwcHC9xvBEIiIi0Ol0x/T85ufnn3I6w/Tp07njjjv45ptvGDVqVAOCFEKcjbRGPW3/bwK536wm55vVaM0GQge2I6hbIrYvT7znfUun1RnqflbKw65NUwCIiO1OeWkmAUGxVFXkUnVoH+UHdlKZsRuN3oDWZCG853n+Cluca5qwvW9Jbb0MexbNJijSzBUv9OGeb0cQGGEmbVEusZ1CTn1gKxES0Q5rcO2wsG2/vg8oanJqr/46Du5HazRhaduBdk+9Qvyke1rMqpni3GY0Gunbty8LFiyod/+CBQsYMmTICY+bNm0akyZNYurUqYwbN66pwxRCNBOtXkfc9UPp8ekfsQ3rTPGyXZhiQtFq5CsngFarJy5lKAA71n+Oo6qAqoocQFFTko/XVQNaLV3ueY4udz+L3to6r5aL09CEWx21pLZervyKZhfdIZjr/zOA965eysI3dsIF/o7o7KDV6ug94kGqKvIoydtNcc4OKkoyAcj/8RsMEVFEjr0cTSscIi7OQEP38juNff8eeughbr75Zvr168fgwYP54IMPyMzM5J577gHg73//O9nZ2XzxRe2WGtOmTeOWW27hjTfeYNCgQXU9yRaLhZAQ6RAToiUwhFhJefASXCWVpL81j2R1tSTAv2nb+TISUs6jtGg/hbnbKS7YDUDR5hVoDSYSL7oRrd5wilqEaF4tpa2X5Fc0iS+2DTxlGeNo2PrdIux2O1Zr69q8ffkPj/hULjAwkKqqKh5//HH+/ve/ExAQ0MSRiZamOfb5vfbaaykqKuLZZ58lJyeHbt26MWfOHJKTa1eCz8nJITMzs678+++/j9vt5r777uO+++6ru//WW2/ls88+a3gAQohG5T2yEo4P8g6HnvRxy8XjKPvnWxQHZBNhSDjDyM4tljX7TviYFQgjjFSGsdXoJse5j1hjO9pa+xCw1gxrdx33OE9ZmU/nXuD95nRCFuewpm7vW0pbL8mvaFbVu9Mpm7sSZ0YOnpJylNdLXl4eqakyf/V49uzZQ2hoKAaDgZdeeomLL76Yfv36+TsscS5phiu/APfeey/33nvvcR/7fSO3ZMmS0zqHEOLc4C4soXzeUmr2HsRdWAJAjdfu56jOXp0DhtIxYBAmrYWcmv2UufOJM7X3d1jiXNMM7X1LaOsl+RXNwlPpoGzOCspmLsWYFE1A/y7oIkKx9u4oie9JxMXVLhAyefJknnzySdLS0pgyZYqfoxLnEo239taQ8kIIcTqUx4N93VZKvpwJeh3WPt0IGNwHU9tk4t6t8Xd4Zy2Dtnbxy3J3EVsrFwFI8isaTNp730jyK5qUp8pB6XeLqFi8HuXxEHrFSEKvHIlGK/N+GuKbb2qHL7Vr187PkYhzTjNd+RVCtF5KKSoXraJ8/jI8RSVYenUh/I6JaK2WujIazR4/RnhuKHLVLnIZrIv0cyTinCTtvU8k+RWNSimFy1Xb+6vR6Sj6eCb2LXsIuXgIQWMGoQ+VVQt9sWDBAiorKxk8eDDFxcWsXr0agDvvvNPPkYlzzmlufSCEECfjcqm6785VK9ZRMnUm1kG9Cb7/FoxJrXdbo4aodJdQ5SnFrAskSBfOHvsaAFIs3fwcmTgnSXvvE0l+RaOoqfHy0Zfl/Onxgto7tE+jDw/BXVBCxD1Xo48MxXkgG02HZHSBlpNV1eotWLCAMWPGAKDX6/n73/8OwIgRI0hIaF2LhYgzd7qb3gshxO8ppZi/yM7fni1i5x4XADrbC3jKKrEO6k3IuJG4cgvQWkzoI8P9HO3Zza1crC6bgQc3AO0sR9fziDKm+CkqcS6T9t43kvyKRhHd7SAVlUcnDwSe1xO8CtU+kYpf1lCzLwsAjcVExB0TCBzSo66sUkr2rP0fnTp1Qq/X43a7cbvdTJw4kS5dunDeebLRvTgNMgxKCNFIXnithKf/VVL3e+f2BrKSuuEpLUPVOMl58rXazxCNhqBRQwmdOK5umpO09fXp0GPVBVPhKQEUJq2VXoGjMOsC0Wnk67k4DdLe+0TeXaJRvPxEBEtX2blwmJXPvipn5fLNdW8qY3IsMX+/DX2UjZJvfqHg7a/B6yXwvF7Yt+xBe4OW4OBgrrvuOl5++WVCQ0P9+lz8LTExkdmzZ7No0SLGjh1Lt27d6NZNhkCJ06SgAbuWtNphUEKIUxs60MLVlznp18vEocMe/vNxGez9FQBtUABhN47H2rsrVWs2U/rNHLzVNdhuuRJVXcOisim4cRKhT6CDpT9BOpufn41/aTQaegaNIrs6jQBdKHGm9tI5IM6MtPc+keRXNIq7bwnh7ltqN6yeMDaQiz6JwxBowhodSHj3GDRaDVCF6jeIDS9Vk/7etwTn7SIywkoeUF5ezgcffMDixYvZs0cWxRgzZkzd0GchzoQMgxJCNJSW4y9KecHQAC4YWrvfvNer6NvDzEtZvTCGBxLcNQ691QhUQ89O5LXxsvvluZicRYQPboMbJwCF7kMUVhzivJBrCdAHN9dT8ot5hR/4OwTRikh77xtJfkWjC7fpaHvl8a9UarQa+j42AmOohUOL9mPPqQCgd+/eHDhwgL179/LOO++ccA8xIUQDKRo4DKrJIhFCtCBarYZbJgbz2Y7jt/fRo7qg0WnJ/Gote1//BQAdBkyaAOyqlB1VyxgQcmlzhixEyybtvU9kvxnR7DRaDT3uHcSgZ0fV3bdp0ya++uorxo0bR6dOnfwYnRBCCCEaQ9TITvR85WqMEYEAeHARb25PsrkbkcZEP0cnhGiN5MqvaHbKq8j8eS/r/lm7kftll13GrFmz+Oqrr/jpp5/8HJ0QLYwsgCGE8JOKPXlsvHcKAIE6G5WeYvY5NjAm/A4/RyZECyTtvU8k+RXNylPj5ocLPqp3365du0hLS2v1C10J0SS8QEPWUGnIYhlCCHECO57+kcIVe+t+r/QUMyh4AjqtwY9RCdGCSXvvE0l+RbPy1HgIiA/G6/LQ477BVGSWUvRTFh06dPB3aEK0SLIAhhDCHwyhVgBSJg0huGs8W//6DVqNlkBdqH8DE6KFkvbeN5L8imaltxpoM74Lldll5K7KpCKrjC7t2/s7LCFaLhkGJYTwA1u/ZDQ6DZUHCrFnFANg0bXs1Z2F8Ctp730iya9oEm0DC457/+qP09j2zna0eg3GAAPVZU7aTbq4maMTohWRxlAI0UA1yuVz2Wr3scOYK9Ny2PH0jwCYokOoySvDqDFj0JsbLUYhxO9Ie+8TSX5FswpLDkRv1gEw/E9dydpQyB13yMIXQjQZaQyFEM1MF2DCnGCj+lAxEaO64SqzE7Ek0N9hCdGySXvvE9nqSDSrjqPiMVr1uKs9FOwp47IXB3Deeef5OywhWi7vadyEEOIMWBJshA2pXcvj8FerSL3/IpKsXf0clRAtnLT3PpHkVzS7lEFRANhSg/wciRAt35EFMBpyE0KIMxXcIwkAa/sYP0ciROsg7b1vZNizaFaZ6wvYOSeLqI4hdLs02d/hCNHyyTAoIUQz8zic7HvpR7QmPSl/HOXvcIRoHaS994lc+RXNyuOqHWMx4i/dMQW17r3+1q1bxx//+EdycnL8HYpoybyq4TchhDgTSqE8XiJGdyewY5y/o/GrvLw8Hn74YRYvXuzvUERLJ+29T+TKr2gWZdlVaHQaUgZFEdEumPVf7iN1cLS/w2p2LpeLqVOn8vPPPzN16lQAunTpwgMPPODnyESLJT3BQohm4q6sxllYgTUlkqhLepE/dzNJd45EZzb6O7Rmt2jRImbMmMFbb70FQFpaGiNHjvRzVKJFk/beJ5L8iia3bWY6c57cgM6opcvFiRTuK6dwXzlKKTQajb/DazZ5eXl07tyZkpKSuvvuvvturr/+ej9GJYQQQpw5e0Yh2+/7BOWuveJbuGAbULvtUUjP1jPNyePxcOONNzJ9+vS6+zp16sRzzz3nx6iEEEdI8isaZIHDt3+ZjXf3BMDjcbJi83y0WgMep4udsw7TJn4EJmMwm+7pVVt4VRMFe5ZZu3ZtvcT3ySef5JlnnvFjRKJ1aGBPMK2zJ1gIcdTbpR18LqumRAKQvWwuGvQonBQu2EZku0GYAm0EbOiG2mbGW5bZVOGeVaqqquolvueddx6LFi3CYGjdU71Ec5D23heS/IomUeOsIDN3FRVVubjcdpSqnes7pNeDmI2tc5XnsWPH1v185ZVX8uSTT/oxGtFqyDAoIUQT8bi95OxciqMsl+LMLXX3pw65nsg2/fwYmf8EBwfTu3dvNm3aREpKCt9++60kvqJ5SHvvE0l+RZOosOeSfngFAF3ajEcpL0EBsa0y8d20aRMHDx5k9OjRlJWVkZaWRu/evdHpdP4OTbQGXkWDendb6QIYQoiGc9V4ydr4IwBRHYcSFNUWrVZLaHzr29O3tLSUqVOncvXVV7N27Vo2bdpEx44dCQ4O9ndoorWQ9t4nstqzaBI1zgoA2iWOIj6qLwnR/QkJTPBzVP7xj3/8g6uuuoo333yT4OBg+vfvj14v/U6imShvw29CCOGD4kMOAIKi25LS/0rCk3sSltgdjbb1fb2cNm0a9913H9dccw16vZ7+/ftL4iual7T3Pml9n06iyRUXuNl5YAZxkX1IiTvP3+H4XYcOtXOnDhw44OdIRKt0ZBhUQ25CCHEKSim+emw7ltAY2p8/yd/h+F1KSgoAe/fu9W8govWS9t4ncvlJNLqPXswDIDX+fDQa6V95/vnnGT58uGxxIPxDhkEJIZrA5jm55KRVkDroYvQmq7/D8buxY8eyYMECkpNbz8rW4iwj7b1PJPkVjS4gSIdOa8Rqtvk7lLOC1WplwoQJ/g5DtFayAIYQogkYzLXrVlht8X6O5Oyg0WgYNWqUv8MQrZm09z6R5Fc0qr3bHSyeWUaUrbu/QxFCQG0ncIMawyaLRAjRQtjLXCz64ABhcWYsIdH+DkcIAdLe+0iSX9Egf91+9Qkfc+zP4eCTX2KMiWHn2iWEhoY2X2BCiOOTnmAhxG/yPZU+lXtn8/ATPuYpqyTvjWm4DrmIeew2Ij4t9KnOudVf+lROCHGapL33iSS/olGUr91DxvPTMSdFkfLU9ZL4CnG28HqBBqzo6G2dqz8KIU7NXVJO9mNvgcdL9F9vwdQmASjxd1hCCJD23keS/Ioz5q5wkP2fnwjq257k/7sGjb5171/r9XrRtsJtHoQQQrRcSikKP5qBRqcl/pU/owsJ9HdIfiVtvRDnJnnXigar3HyAksVb2X71i1TtyqJwxiq8NS7i7xvXqhPfwsJCgoKCCAsLw9tKe9PEWUi2PhBCnAbnoXyqNuwi8/5XKJ21jOrd6Tg2pRF+2+WtOvH1eDyMHz8enU7Hnj17/B2OEEdJe+8TSX5Fgx188ksOvTYT5XRTungr5avTCB7UEUN4kL9D84u33nqL66+/nt69e1NZWUl5eTl2u93fYYnTtHPnTjQaDZ999pm/Q2kc0hgKIU5DyfT55L86BU9xGaXfL8a+Zju60ECs/br4OzS/WLZsGVdeeSXDhg3jxx9/BGD37t1+jkqcic6dO3P55ZejWkq7J+29TyT5FQ0WNqpX3c/F8zZSk1VIUJ+2/gvIz5544gm++uorDh06BMDDDz9MYGDr7RU/18XGxgLw/vvv+zmSRuJVDb8JIVq9wPP71P2sapyU/7waS48OaDQaP0blP++99x4//PADq1atAqBfv36MGTPGz1GJM2G325k1axZOp9PfoTQOae99InN+RYPF3jkGU2IEuZ/+QkCPFKKuOY/Anqn+DqvZORwO7r//fsrKygCIiIjgzTff5Prrr/dzZOJMhIWFkZmZiclk8ncojUIpL0r5Pgy/IWWFEC2XtW9nov96C/n/mQ5A+M2XEDist5+j8o/XX3+dadOm1f3+0EMP8dJLL2EwGPwYlThTu3btIiMjQ9r7VkaSX9EgoR8GAUEE1Qwml1/QHdKQsLYHrP1dwcv8EV3z2r59O5988gkANpuNzMxMLBaLn6MSjSExMdHfITQe1cDe3VY6DEqI1mBOVRufykX9VJsMeD1dKFZGXDWVJBf3QT/32DZOFRT5VOdo3bU+lVvgme5Tueb0xBNP1P28f/9+2rTx7XUUZzer1Urnzp39HUbjkfbeJ5L8Cp8opeqGOpUW7mPX+v+iN1iJjOvh58j85/PPP6/7+auvvpLEV5ydlKJBO9m30sZQCFG/ra+xl7Jn6Se4aqqISO2LVm/0c3T+8eOPP1JZWbs/8vPPPy+Jrzh7SXvvE0l+xSm99GYxj79YxLjRAezb8wM5GasIjWhPp743YDAG+Ds8v1m4cCEAY8aMYfTo0X6ORogT8HpB04ChTa10GJQQrd2BLRU8P3ErliAdtmQL2dsXYjAH0m3snwkIi/d3eH6zePFiACwWCw8++KB/gxHiZKS994kkv+KUxo0O4PEXi5i9oAqoXehBpze16sQX4JlnnmHLli38+c9/9ncoQpyY9AQLIXwQ394KgKPCQ/b2XwBwVVdgCY7yZ1h+N2nSJDQaDTfccANWq9Xf4QhxYtLe+0SSX3FK3TubWPx9Ai+9VcyG3e2wV+ZTWZbt77D8buLEiUycONHfYQhxUsrrRTWgJ7i1LoAhRGtnsur497J+fPNKOrvW29AZTJRkbaOmqhRLcKS/w/Obnj17MnnyZH+HIcQpSXvvG9nqSPjk/MEW5kyNp33Pq3BWl2GL7kxNdZm/wxJCnIrs+yeE8FFYtIk/vNqRzhf8Aa/biSU4GjSalrMPqhAtmbT3PpHkVzRIeUkmbpeDkoI9rF3wPKWF+/wdkhDiZGTfPyFEA3m9Hspy92CwBLF11kukr//e3yEJIU5F2nufNNqw5/KK1nnpvDUoKvYy+f0Sqqq8HD7wKwDVVYUAFBzeSmBIwjHHlJeXN2uMQpzL5P0izhXS1p/bHJXuEz7m9SoWT80le6+djN0zQCkqi2unOJXn7cPtqj7ucW7l9OncHuXyqZx8HoqWSv63zw5nnPwajUZiYmJI7pveCOGIs9/uer/lZqwmN2P1MaVCQp5sroCEaBFiYmIwGptgKxGlgIas/tg6e4LFyUlb31Ic8LFcbQe31+UAoLq8gA3f/KOJYqovJOSHZjmPEP7QZG09SHvvozNOfs1mMwcPHsTp9K3nTwghxLGMRiNms7nR61VehdL43sDJ3D5xPNLWCyHEmWuqth6kvfdVowx7NpvNTfaHFEIIcQaUl4b1BMuwVnF80tYLIcRZTNp7n8hWR0II0YJJT7AQQgjR8kl77xtJfoUQogVzq5oG9e668W1RGiGEEEKcPaS9940kv0II0QIdWaBoRe6cBh/bpAtyCCGEEKLRSHvfMBrVWq95CyFEC1ddXX1aCxQ15YIcQgghhGhc0t77TpJfIYQQQgghhBAtntbfAQghhBBCCCGEEE1Nkl8hhBBCCCGEEC2eJL9CCCGEEEIIIVo8SX6FEEIIIYQQQrR4kvwKIYQQQgghhGjxJPkVQgghhBBCCNHiSfIrhBBCCCGEEKLFk+RXCCGEEEIIIUSLJ8mvEEIIIYQQQogWT5JfIYQQQgghhBAtniS/QgghhBBCCCFaPEl+hRBCCCGEEEK0eJL8CiGEEEIIIYRo8ST5FUIIIYQQQgjR4knyK4QQQgghhBCixZPkVwghhBBCCCFEiyfJrxBCCCGEEEKIFk+SXyGEEEIIIYQQLZ4kv0IIIYQQQgghWjxJfoUQQgghhBBCtHiS/AohhBBCCCGEaPEk+RVCCCGEEEII0eJJ8iuEEEIIIYQQosWT5FcIIYQQQgghRIsnya8QQgghhBBCiBZPkl8hhBBCCCGEEC2eJL9CCCGEEEIIIVo8SX6FEEIIIYQQQrR4kvwKIYQQQgghhGjxJPkVQgghhBBCCNHiSfIrhBBCCCGEEKLFk+RXCCGEEEIIIUSLJ8mvEEIIIYQQQogWT5JfIYQQQgghhBAtniS/QgghhBBCCCFaPEl+hRBCCCGEEEK0eJL8CiGEEEIIIYRo8ST5FUIIIYQQQgjR4kny24SmTp3K66+/ftzHNBoNTz/9dLPGI87ckiVL0Gg0LFmypNHqTE9PR6PR8NlnnzVanf9rzpw5J/xfS0lJYdKkSU1y3iNWrlzJ008/TWlp6TGPjRgxghEjRjTp+c8FGo2G+++/399hCNHknn76aTQaDYWFhacse7zPh9+3nU3xmSzOLU3RjkyaNImUlJRGrfMIu93O008/fdz/2c8++wyNRkN6enqTnPuIF154gRkzZhxzv7yfajXkc0qce/T+DqAlmzp1Ktu3b+fBBx885rFVq1aRkJDQ/EGJVmfOnDm8/fbbx02Af/jhB4KDg5v0/CtXruSZZ55h0qRJhIaG1nvsnXfeadJzCyHOXb58PvTp04dVq1bRpUuXZohIiDNnt9t55plnAI5J2seNG8eqVauIjY1t0hheeOEFrr76aiZMmFDvfnk/idZAkl9qP4isVmuznnPQoEHNer4TUUpRXV2NxWLxdyjCD3r37u3X80sDK4Q4EV8+H4KDg8+a9vREGvodwx/fScTZITIyksjISL+d/1x4Pwlxpho87Hn37t1cf/31REdHYzKZSEpK4pZbbqGmpqauzPbt2xk/fjxhYWGYzWZ69erF559/Xq+eI0Mrpk2bxuOPP05cXBzBwcGMGjWKtLS0emU3bdrEpZdeSlRUFCaTibi4OMaNG8ehQ4fqyiileOedd+jVqxcWi4WwsDCuvvpqDhw4UK+uESNG0K1bN5YtW8aQIUOwWq3cfvvtTJgwgeTkZLxe7zHPeeDAgfTp06fu97fffpvzzz+fqKgoAgIC6N69O6+88goul6veeWbPnk1GRgYajabudsT/Dt3asmULGo2Gjz/++Jhzz507F41Gw48//lh33969e7nhhhvqXo/OnTvz9ttvH/fv9XtHhle+9957dO7cGZPJVPe38aXeI3+3qVOn8re//Y3Y2FgCAwO57LLLyMvLo6Kigj/84Q9EREQQERHBbbfdRmVlZb06fHn9jryG3bp1Y/ny5QwaNAiLxUJ8fDxPPPEEHo+nrtyRYcOvvPIKzz//PElJSZjNZvr168fChQuPeQ18ff12797N2LFjsVqtREREcM8991BRUeHT63wihw8fZuLEiQQFBRESEsK1115Lbm7uccuuX7+eyy+/HJvNhtlspnfv3nz99df1ytjtdh555BFSU1Mxm83YbDb69evHtGnTgNqhW0ee2//+Hx4ZUvX7Yc8NeV8uWLCA8ePHk5CQgNlspl27dtx99931hgk9/fTT/PWvfwUgNTW17vxHhlQdb7hacXEx9957L/Hx8RiNRtq0acPjjz9e7zPmyPO5//77+e9//0vnzp2xWq307NmTn3766ZR/h+b8P/bl8+v3lFL83//9HwaDgQ8//PCUz0eIc01WVhZXXnklwcHBhISEcNNNN1FQUFCvjC/DWY83THPSpEkEBgayb98+LrnkEgIDA0lMTOThhx8+5nPE6XTy3HPP0alTJ0wmE5GRkdx2223HxDJ9+nTGjBlDbGwsFouFzp0789hjj1FVVVWv3JFzb9u2jTFjxhAUFMSFF154wviPDK/cuHEjV199NWFhYbRt2xZo+PeaVatWMWTIECwWCykpKXz66acAzJ49mz59+mC1WunevTvz5s2rd/y+ffu47bbbaN++PVarlfj4eC677DK2bdt23Nd6ypQpPPTQQ8TExGCxWBg+fDibNm067uuwY8cOLrzwQgICAoiMjOT+++/HbrfXK+vr81RK8corr5CcnIzZbKZPnz7MnTv3hK+trz777DM6duxY933giy++OG45X/9XFi1axIgRIwgPD8disZCUlMRVV12F3W4nPT29Lrl95pln6trEI+3w8YY9H/n7rlu3jmHDhmG1WmnTpg0vvfRSve+s1dXVPPzww/Tq1YuQkBBsNhuDBw9m5syZ9eLTaDRUVVXx+eef153/yPvsRMOef/zxRwYPHozVaiUoKIjRo0ezatWqemWO/C/v2LGD66+/npCQEKKjo7n99tspKys75d+huf6PvV4vzz33HB07dsRisRAaGkqPHj144403Thrf7t27adOmDQMHDiQ/P/+Uz0ecxVQDbN68WQUGBqqUlBT13nvvqYULF6opU6aoiRMnqvLycqWUUrt371ZBQUGqbdu26osvvlCzZ89W119/vQLUyy+/XFfX4sWLFaBSUlLUjTfeqGbPnq2mTZumkpKSVPv27ZXb7VZKKVVZWanCw8NVv3791Ndff62WLl2qpk+fru655x61c+fOuvruuusuZTAY1MMPP6zmzZunpk6dqjp16qSio6NVbm5uXbnhw4crm82mEhMT1VtvvaUWL16sli5dqmbOnKkAtWDBgnrPedeuXQpQb775Zt19f/nLX9S7776r5s2bpxYtWqRee+01FRERoW677ba6Mjt27FBDhw5VMTExatWqVXW3IwD11FNP1f3eu3dvNXTo0GNe84kTJ6qoqCjlcrnq6g0JCVHdu3dXX3zxhfr555/Vww8/rLRarXr66adP+TcEVHx8vOrRo4eaOnWqWrRokdq+fbvP9R75uyUnJ6tJkyapefPmqffee08FBgaqkSNHqtGjBmCLGQAA9KpJREFUR6tHHnlE/fzzz+rll19WOp1OPfDAA/Vi8OX1O/K3Cg8PV3FxcerNN99U8+fPV3/6058UoO677766cgcPHlSASkxMVOedd5767rvv1DfffKP69++vDAaDWrlyZb2/iy/PMzc3V0VFRan4+Hj16aefqjlz5qgbb7xRJSUlKUAtXrz4lK/179ntdtW5c2cVEhKi3nrrrbrnc6TOTz/9tK7sokWLlNFoVMOGDVPTp09X8+bNU5MmTTqm3N13362sVquaPHmyWrx4sfrpp5/USy+9pN566y2llFL79u1TV199tQLq/R9WV1crpZRKTk5Wt9566zF/31O9L5VS6t1331Uvvvii+vHHH9XSpUvV559/rnr27Kk6duyonE6nUkqprKws9cADDyhAff/993XnLysrq/sbDx8+vK5Oh8OhevTooQICAtS///1v9fPPP6snnnhC6fV6dckll9R7PY/EOWDAAPX111+rOXPmqBEjRii9Xq/2799/0r9Fc/0f+/r59b//09XV1eq6665TQUFBau7cuSd9HkKca5566qm6995f//pXNX/+fDV58mQVEBCgevfuXffZodSxnw9KHdt2Hnkv/+9n8q233qqMRqPq3Lmz+ve//61++eUX9eSTTyqNRqOeeeaZunIej0eNHTtWBQQEqGeeeUYtWLBAffTRRyo+Pl516dJF2e32urL//Oc/1WuvvaZmz56tlixZot577z2VmpqqRo4cWS++W2+9VRkMBpWSkqJefPFFtXDhQjV//nyfXo+//e1vasGCBWrGjBlKqYZ9rwkPD1cdO3ZUH3/8sZo/f7669NJLFaCeeeYZ1b17dzVt2jQ1Z84cNWjQIGUymVR2dnbd8UuXLlUPP/yw+vbbb9XSpUvVDz/8oCZMmKAsFovavXv3Ma91YmKiGj9+vJo1a5aaMmWKateunQoODq73uXvkb5CUlKSef/559fPPP6unn35a6fV6demll9Z7DXx9nkdeqzvuuEPNnTtXffDBByo+Pl7FxMQc83/iq08//VQBxzyfxMRElZycXFfO1/+VgwcPKrPZrEaPHq1mzJihlixZor788kt18803q5KSElVdXa3mzZtX9zyOtIn79u2rF8/BgweP+fu2b99evffee2rBggXq3nvvVYD6/PPP68qVlpaqSZMmqf/+979q0aJFat68eeqRRx5RWq22XrlVq1Ypi8WiLrnkkrrz79ixo97f+H/fT19++aUC1JgxY9SMGTPU9OnTVd++fZXRaFTLly8/5u/TsWNH9eSTT6oFCxaoyZMnK5PJdMz3u+Nprv/jF198Uel0OvXUU0+phQsXqnnz5qnXX3+93nfAI8+loKBAKaXUkiVLVFhYmBo/fryqqqo65XMRZ7cGJb8XXHCBCg0NVfn5+Scsc9111ymTyaQyMzPr3X/xxRcrq9WqSktLlVJH32C//0L79ddf131RV0qp9evXK6CuMTieVatWKUC9+uqr9e7PyspSFotFPfroo3X3DR8+XAFq4cKF9cq6XC4VHR2tbrjhhnr3P/roo8poNKrCwsLjntvj8SiXy6W++OILpdPpVHFxcd1j48aNq/fh+b9+34C/+eabClBpaWl19xUXFyuTyfT/7N13eBR1/sDx9/bNZtN7T+i9N2mKCIiCigWsCHaxnOKdp1f0d3eWU89eUE8Ru2dBBRWR3nvvNQ3Se986vz9ioiEJ2Q3ZlOXzep59HnbmOzPf2Szz2c/MtyiPPPJI7bJJkyYpsbGxtclDjfvvv18xGo11jt/YcQMCAuqVc3W/NX+3qVOn1in30EMPKYDy4IMP1ll+1VVXKcHBwY3W52yfX83f6vvvv6+zzZ133qmo1WolNTVVUZTfkt/o6GilsrKytlxJSYkSHBysXHLJJW6f55///GdFpVIpu3fvrlNuwoQJzU5+582b1+j5nJnU9ujRQxk4cGDtTY8aU6ZMUaKiohSHw6EoiqL06dNHueqqq8563Pvuu09p7D5XY8lvU/8vz+R0OhWbzaakpqbWO8cXXnihXjCvceaP27ffflsBlC+//LJOueeee04BlF9++aV2GaBERETU3nhTlOqbFmq1Wnn22WcbrOeZ5+np77Er16+ac7nvvvuU/Px8ZfTo0UpMTEy9754Q3qDmR+XDDz9cZ3nND+xPPvmkdtm5JL8NXUcuu+wypXv37rXvP//8cwVQvvnmmzrltm3bpgDKW2+91eA51Fzv1qxZowDKnj176h17/vz5Z/0catR8Hk888USd5c35XbN9+/baZfn5+YpGo1F8fHzqJAi7d++ud0P/THa7XbFarUrXrl3r/J1qPutBgwYpTqezdnlKSoqi0+mUO+64o97n8Oqrr9bZ99NPP60Ayvr16906z8LCQsVoNCrTpk2rU27Dhg0K0Kzk1+FwKNHR0Y2ez+9/v7n6Xfn6668V4KzX79zc3Hrf4xqNJb+AsmXLljple/XqpUyaNKnR49jtdsVmsym33367MnDgwDrrfH1968T+Gmf+f6r5jPr27Vv7u0NRFKW0tFQJDw9XRo4cWbus5rv8/PPP19nnnDlzFKPRWOczbkhrfY+nTJmiDBgw4Kx1+X3y+/HHHyt6vV558MEH63wGouNyudlzRUUFa9asYfr06Wftj7By5UrGjx9PXFxcneWzZs2ioqKiXjOJK664os77fv36AZCamgpAly5dCAoK4s9//jNvv/02Bw8erHfMH374AZVKxc0334zdbq99RUZG0r9//3rNN4KCgrj44ovrLNNqtdx8880sXLiwtnmGw+Hg448/5sorryQkJKS27K5du7jiiisICQlBo9Gg0+mYOXMmDoeDo0ePNvrZnM1NN92EwWCoM+Lv559/jsViYfbs2UB1k5YVK1Ywbdo0TCZTnXO97LLLqKqqYvPmzU0e6+KLLyYoKKj2fXP2O2XKlDrve/bsCVQP1nDm8oKCgjpNRt35/Pz8/Op9R2688UacTidr166ts/zqq6/GaDTW2Xbq1KmsXbsWh8Ph1nmuWrWK3r17079//3rHbq5Vq1Y1ej6/d/z4cQ4fPsxNN90EUK+emZmZtU2Qhw0bxpIlS3jsscdYvXo1lZWVza7f7zX1/xIgJyeHe+65h7i4OLRaLTqdjoSEBAAOHTrUrOOuXLkSX19frr322jrLa5qEndmMfdy4cfj5+dW+j4iIIDw8vE49z8bT32NXrl81kpOTueCCCygpKWHz5s31vntCeJOa61uN6dOno9VqWbVqVYvsX6VSMXXq1DrL+vXrV+fa8MMPPxAYGMjUqVPrXGcHDBhAZGRknd8OJ0+e5MYbbyQyMrL2//uFF14INHy9u+aaa9yq75nl3f1dExUVxeDBg2vfBwcHEx4ezoABA4iOjq5dXnON+/3nYLfbeeaZZ+jVqxd6vR6tVoter+fYsWMNntuNN95YpxtXQkICI0eObPBvd+bfuSbe1ZR19Tw3bdpEVVVVvf2NHDmyNu6468iRI2RkZDR6Pr/n6ndlwIAB6PV67rrrLj788MN6TbebKzIykmHDhtVZdub3GeCrr75i1KhRmM3m2rj8/vvvNzsm13xGt9xyC2r1bymD2WzmmmuuYfPmzfWasTf0+6GqqsqlpsKt8T0eNmwYe/bsYc6cOSxdupSSkpJG6/P0008za9Ys/v3vf/Pqq6/W+QxEx+XyX7GwsBCHw9HkCMX5+fkNjlJX86XNz8+vs/z3SSWAwWAAqP0RHxAQwJo1axgwYAB/+ctf6N27N9HR0Tz55JO1feuys7NRFIWIiAh0Ol2d1+bNm+sNVd7YKHq33XYbVVVVfPHFFwAsXbqUzMzM2uQTIC0tjTFjxnD69GleffVV1q1bx7Zt22r7VTY3+QgODuaKK67go48+qu3PumDBAoYNG0bv3r2B6s/Obrfz+uuv1zvPyy67DMClYdnPPP/m7Dc4OLjOe71ef9blVVVVgPufX0RERL36R0ZG1ta7oeVnLrNarZSVlbl1nvn5+Y3ur7ny8/PPej41srOzAfjjH/9Yr55z5sypU8/XXnuNP//5z3z33XeMGzeO4OBgrrrqKo4dO9bsekLT/y+dTicTJ05k4cKFPProo6xYsYKtW7fW3jxo7v+Dms/99z9EAMLDw9FqtU1eP2rq6urxPf09duX6VWPr1q0cPXqUGTNmyEjwwuuded3TarWEhITU+z/eXCaTqc7NUKi+NtT8H4bqa21RURF6vb7etTYrK6v2OltWVsaYMWPYsmULTz31FKtXr2bbtm0sXLgQqH+9M5lMbo+if2Zcdvd3zZnXLKi+bjV1LQOYO3cuf//737nqqqtYvHgxW7ZsYdu2bfTv37/Ba2ljsfHMv13N37ShbWvKunqeNeVbMi67s09XvyudO3dm+fLlhIeHc99999G5c2c6d+7cZH/SprgS6xYuXMj06dOJiYnhk08+YdOmTWzbtq32t21z1HxGjf2udzqdFBYWnrWuZ/5+OJvW+B4//vjj/Oc//2Hz5s1MnjyZkJAQxo8fz/bt2+sd+5NPPiEmJobrr7++ybqLjsPl0Z6Dg4PRaDRnHaQFqr/0mZmZ9ZZnZGQAEBoa6mYVoW/fvnzxxRcoisLevXtZsGAB//znP/Hx8eGxxx4jNDQUlUrFunXrav+T/d6Zy878YV2jV69eDBs2jA8++IC7776bDz74gOjoaCZOnFhb5rvvvqO8vJyFCxfWudu4e/dut8/rTLNnz+arr75i2bJlxMfHs23bNubNm1e7PigoCI1Gwy233MJ9993X4D6SkpKaPM6Z599S+3WFu59fTSL4ezUDRJ15gW1o4KisrCz0ej1msxmdTufyeYaEhDS6v+YKCQlh69atTe6z5v/I448/ztVXX93gvrp37w6Ar68v//jHP/jHP/5BdnZ27VPgqVOncvjw4WbXtSn79+9nz549LFiwgFtvvbV2+fHjx89pvyEhIWzZsgVFUep8T3NycrDb7c26fniCO9/jpq5fNWbMmEFkZCR//etfcTqd/O1vf2uNUxGiTWRlZRETE1P73m63k5+f3+CPfE8JDQ0lJCSk3sA5NWpalaxcuZKMjAxWr15d+7QXaHDucmj8N8bZnLmNu79rzsUnn3zCzJkzeeaZZ+osz8vLqzc9HTQea8/82zX0Nz0zfrt6njXlGzt2c+bkbWqfv+fqdwVgzJgxjBkzBofDwfbt23n99dd56KGHiIiI8GgS9cknn5CUlMT//ve/Ot+nMwd5c0fNZ9TY73q1Wl2nJWFbcvV7rNVqmTt3LnPnzqWoqIjly5fzl7/8hUmTJpGenl5npPWff/6ZGTNmMGbMGFasWNHsVgaifXH5yW/NiH5fffXVWZ8ujh8/vjZQ/N5HH32EyWQ6pyHUVSoV/fv35+WXXyYwMJCdO3cC1U0XFUXh9OnTDBkypN6rb9++Lh9j9uzZbNmyhfXr17N48WJuvfVWNBpNnTpA3cCjKEqDI7K68wQKYOLEicTExPDBBx/wwQcfYDQaueGGG2rXm0wmxo0bx65du+jXr1+D59qcHw6e2m9D3Pn8AEpLS+uMdA3V8yer1WrGjh1bZ/nChQvr3AUsLS1l8eLFjBkzBo1G49Z5jhs3jgMHDrBnz556x26ucePGNXo+v9e9e3e6du3Knj17GqzjkCFD6gTaGhEREcyaNYsbbriBI0eO1DZFcueuq6sa+jsCvPPOO/XKunP88ePHU1ZWxnfffVdnec3om2cbNbU1ufs9rtmmoevX7/3tb3/jlVde4YknnuDxxx9v+YoL0U58+umndd5/+eWX2O32Jkd3bklTpkwhPz8fh8PR4HW25iajO9e7lqxbS/2uaYpKpap3bj/++COnT59usPznn3+Ooii171NTU9m4cWODf7sz/8418a6mrKvnOWLECIxGY739bdy40eVuLmfq3r07UVFRjZ7P77n6Xfk9jUbD8OHDa1sE1VzzPRGTofrvqNfr6yS+WVlZ9UZ7rqmDK8fv3r07MTExfPbZZ3U+o/Lycr755pvaEaDbA3e/xwCBgYFce+213HfffRQUFNQZZRuqm8DX3JgZM2bMObeqE+2DW/P8vvTSS4wePZrhw4fz2GOP0aVLF7Kzs1m0aBHvvPMOfn5+PPnkk/zwww+MGzeOJ554guDgYD799FN+/PFHnn/+eQICAtyq4A8//MBbb73FVVddRadOnVAUhYULF1JUVMSECRMAGDVqFHfddRezZ89m+/btjB07Fl9fXzIzM1m/fj19+/bl3nvvdel4N9xwA3PnzuWGG27AYrHUmQYGYMKECej1em644QYeffRRqqqqmDdvXr1mH1D9xGfhwoXMmzePwYMHo1arGTJkSKPH1mg0zJw5k5deegl/f3+uvvrqep/Xq6++yujRoxkzZgz33nsviYmJlJaWcvz4cRYvXszKlStdOs8zeWq/Z3Ln84Pqu4733nsvaWlpdOvWjZ9++on//ve/3HvvvcTHx9cpq9FomDBhAnPnzsXpdPLcc89RUlJSO5m8O+f50EMPMX/+fC6//HKeeuopIiIi+PTTTxt8mrp69WrGjRvHk08+WTt9VUNmzpzJyy+/zMyZM3n66afp2rUrP/30E0uXLq1X9p133mHy5MlMmjSJWbNmERMTQ0FBAYcOHWLnzp189dVXQPU0XFOmTKFfv34EBQVx6NAhPv744zoBqebHw3PPPcfkyZPRaDT069evttlQc/To0YPOnTvz2GOPoSgKwcHBLF68mGXLltUrW3P8V199lVtvvRWdTkf37t0bTOBnzpzJm2++ya233kpKSgp9+/Zl/fr1PPPMM1x22WVccsklza5zS3L1e+zK9etMf/jDHzCbzdx1112UlZXx2muvNetJkhDt2cKFC9FqtUyYMIEDBw7w97//nf79+zN9+vRWq8P111/Pp59+ymWXXcYf/vAHhg0bhk6n49SpU6xatYorr7ySadOmMXLkSIKCgrjnnnt48skn0el0fPrpp/Vujraklvxd05QpU6awYMECevToQb9+/dixYwcvvPBCo90vcnJymDZtGnfeeSfFxcU8+eSTGI3Gejfs9Ho9L774ImVlZQwdOpSNGzfy1FNPMXnyZEaPHu3WeQYFBfHHP/6Rp556ijvuuIPrrruO9PR0/u///q/BZssXXXQRa9asqZOwnUmtVvOvf/2LO+64o/Z8ioqKGtynq9+Vt99+m5UrV3L55ZcTHx9PVVUV8+fPB6iNX35+fiQkJPD9998zfvx4goODCQ0NbdbT69+bMmUKCxcuZM6cOVx77bWkp6fzr3/9i6ioqHpJW9++fVm9ejWLFy8mKioKPz+/BhN4tVrN888/z0033cSUKVO4++67sVgsvPDCCxQVFfHvf//7nOrcklz9Hk+dOpU+ffowZMgQwsLCSE1N5ZVXXiEhIYGuXbvW229UVBRr1qxh0qRJjB07lmXLltGnT5/WOi3hCe6OkHXw4EHluuuuU0JCQmqHsZ81a1bt1CmKoij79u1Tpk6dqgQEBCh6vV7p379/nZFsFeW3EeW++uqrOstrRu6tKX/48GHlhhtuUDp37qz4+PgoAQEByrBhw5QFCxbUq9v8+fOV4cOHK76+voqPj4/SuXNnZebMmXVGjrvwwguV3r17n/Ucb7zxRgVocOohRVGUxYsXK/3791eMRqMSExOj/OlPf1KWLFlSb8TJgoIC5dprr1UCAwMVlUpVZ8RdGhnp7+jRowrQ4LRLv/+MbrvtNiUmJkbR6XRKWFiYMnLkSOWpp54663nVHPf30wS5u9/G/m41IxRu27atzvIzh4tXFNc/v5q/1erVq5UhQ4YoBoNBiYqKUv7yl7/UGQW55jvz3HPPKf/4xz+U2NhYRa/XKwMHDmxwiglXP7+DBw8qEyZMUIxGoxIcHKzcfvvttVNi/b6eixcvVgDl7bffbvyD/9WpU6eUa665RjGbzYqfn59yzTXXKBs3bqw32rOiKMqePXtqp7rS6XRKZGSkcvHFF9c5zmOPPaYMGTJECQoKUgwGg9KpUyfl4YcfrjM6ucViUe644w4lLCys9ntYM5JkY6M9N/X/8vefj5+fnxIUFKRcd911SlpaWoPf7ccff1yJjo5W1Gp1nc+vodFc8/PzlXvuuUeJiopStFqtkpCQoDz++ON1rjGK0vh3+cxzakhrfY9dvX41dC6ff/65otVqldmzZ7f7ESbXrFlTOxI5oHz77bdNbrN69Wpl0KBBisFgUJKSkpR58+Z5vqKizdX8X9qxY4cyderU2mvhDTfcoGRnZ9cpey6jPfv6+jZ67N+z2WzKf/7zn9r/y2azWenRo4dy9913K8eOHastt3HjRuWCCy5QTCaTEhYWptxxxx3Kzp07610XGzt2U5/H768tv3cuv2sSEhKUyy+/vN7yM683hYWFyu23366Eh4crJpNJGT16tLJu3bp6n3/NZ/3xxx8rDz74oBIWFqYYDAZlzJgxderz+89h7969ykUXXaT4+PgowcHByr333quUlZU16zydTqfy7LPPKnFxcYper1f69eunLF68uMHvyeDBg5XIyMgGP9Mzvffee0rXrl0VvV6vdOvWTZk/f75y66231putw5XvyqZNm5Rp06YpCQkJisFgUEJCQpQLL7xQWbRoUZ19LV++XBk4cKBiMBgUoDZmNTbac0N/34bq+O9//1tJTExUDAaD0rNnT+W///1vg9/73bt3K6NGjVJMJlOd0bIb+v+kKIry3XffKcOHD1eMRqPi6+urjB8/XtmwYUOdMo19lxs6p4a01vf4xRdfVEaOHKmEhobW5jG33367kpKSctZzKSoqUkaNGqUEBwfX+41wvvCWWK9SlLPcFhOiDV100UXk5eWxf//+s5ZLSUkhKSmJF154gT/+8Y+tVLvfPProo3z++eccO3as3gArQpwPlixZwoYNGxg0aBDXXHMN3377LVdddVWj5ZOTk+nTpw933nknd999Nxs2bGDOnDl8/vnnbo+SK4RoHTWtnL766qt6I/KfadasWXz99dd1RshvLaWlpQQHB/PKK680Or6HEMJ93hLr3Wr2LISob9WqVfz973+XxFectyZPnszkyZNdLv/2228THx/PK6+8AlRPW7F9+3b+85//SPIrhDgna9euJSYmhjvvvLOtqyKEV/GWWC/JrxDnaNu2bW1dBSEaVFVVhdVqdXs75YzRtqF6gJSWGmF206ZNdUbRB5g0aRLvv/8+NpsNnU7XIscRQpx/Lr/88npztQvh7dpjvG+vsV6SX9Fu1Uwa35TExMSzDmohxPmoqqqKpAQzWTkOt7c1m831mis2NaCbO7KysurNeR0REYHdbicvL6/RudiFEG3noosucjnWLliwgAULFni2QkIIoP3G+/Ya6yX5FUIIL2S1WsnKcZC8IwF/P5dntaOk1EnS4FTS09Px9/evXd6S84pC/XlNa35Uy8jWQgghhOvac7xvj7Fekl8hhPBivubql6scvz7Y8ff3rxMMW1JkZCRZWVl1luXk5KDValtsTnEhhBDifNLe4n17jfWS/AohhBdzouDE9W4B7pRtrgsuuIDFixfXWfbLL78wZMgQ6e8rhBBCNEN7i/ftNda3SPLb3E7WQgghqun1eo+MGO7EidPN8u4qKyvj+PHjte+Tk5PZvXs3wcHBxMfH8/jjj3P69Gk++ugjAO655x7eeOMN5s6dy5133smmTZt4//33+fzzz90+tmg9EuuFEOLceCrWg+fjvbfE+nNOfs+lk7UQQohqkZGRJCcnt3hQdCgKDjcGhHOnbI3t27czbty42vdz584F4NZbb2XBggVkZmaSlpZWuz4pKYmffvqJhx9+mDfffJPo6Ghee+01meaoHZNYL4QQ585TsR48H++9JdarlHMcJrekpISAgABSdyS61claCHF+cad5TbKtwqVydx++scky9qIK9t29AB+/cDoNuw6DObjRsivmP+ByHVtSSUkJcXFxFBcXt1i/m5prc/rhGLcHwIjrcbpF6yI6Pon1QghXWRS7S+VS7a61JPlb6pVNllEUhXUPLcGalU/4PVdh6pV01vK7r37UpWO3JE/E+pr9Srx3XYv1+fX3U+Pvp2mp3QkhvIw7zWvMNtcu3lrfpkckTP9gNSqVmp4X3YnWYDprWW+8+Le3PkCiY5NYL4RoikVxLd6b7a7Fep2vvskyWWtPYDmWTtTjMzEN6NpkeYn352+8lwGvhBBeqzI9n5yfdhPf7/ImE19v5UTBIcFQCCGEl3LaHRx5dyM+/Tq7lPh6K4n3rpHkVwjhVZx2B2WHTlO07ST5qw9iCPMnsuuotq5Wm5E7wUIIIbyNoiiUpRaSuyWFrNUnKD9dTOz9TXeF8mYS710jya8QwisoikLmV1vI+GITjgoL2gATAYOTiLlhJOoV5+/0Oa0x4JUQQgjRWvJ2pLPv+ZVUZpWg1msIGRjL4Kcupygxqq2r1qYk3rtGkl8hRIdnySoic+FWshftJOLKwYSO74Nvl0hUalVbV63NOX99uVNeCCGEaG9spRYyVx3j4OtrCeobRZ+5FxE8IAaNoTqdKcpq4wq2MYn3rpHkVwjRYdnLLZx4YTFFm4+j0mtJuPcSIq8c0tbValccbvYBcqesEEII4WmKonD8w20c/3gbit1J9ITu9H10PBq9DL73exLvXSPJrxCiVSTbXZu+CODS7x5pdJ3TZqN8526qDh/FumseiuKkb/BEInw6o/1BDz9U1ttmSVrj+/N2DqX65U55IYQQormO220ulZu6aO5Z11ceOkLlvoM4Nn9GhbWATiEXEB84EGO6PzxQ/8nl8UN/a2aNvYPEe9dI8iuE6DDshUXkzP8Qa9op9HExxPr2Js7cF5M2oK2r1m5JMyghhBAdiWK3U/DVd5Rt2Iw2NIRIn1j6RF5KsCm+ravWrkm8d40kv0KIds9eUEDpxi2UbtiESqcj6pEHMcTH0f2F9LauWrvnRIUD1/s+O90oK4QQQrQUZ1UV5dt3UbpmPbbcPIJvvA6/kcPp+UZeW1etQ5B47xpJfoUQ7ZbTaqNg4feUbdqCSq/HPHQQgZMnovHza+uqdRhOpfrlTnkhhBCitSiKQtmGLRR+uxjFasWnTy9Cbp6BIUGe9LpD4r1rJPkVQrRLtpxccuZ/hD03l+BpV2C+YBhqg6GtqyWEEEKIFuK0WCj44hvKt+3EPHI4AZMnoA0KbOtqdUgON5/8ulPWm0jyK4Rod8p37ibv86/Q+PsRNfdB9DHRbV2lDkuCoRBCiPbImplF7nsf4SgsIvTWG/EdOqitqyTOA5L8CiHalcXfVpK74BNMgwYQOuNa1D7Gtq5ShybJrxBCiPbmVKqdrBdfRxsUSNSjD6GLDG/rKnV4Eu9dI8mvEOKcPJLl2p3aXz4f3mQZS342qf+bR7i5KwMrLkW1oBwob7T8krRXXKzl+cupqHAqbgyA4UZZIYQQ54+7To10qdz6xf3Put5pqeL0VwvQOsoYmXgX2jUGoKrR8ksOPetONc9bEu9dI8mvEKJdKD68m8wl/0PnH0ivoEmoVOfnRbmlyZ1gIYQQ7YUlJ4uMrxZgLy2mf/cb0GplLI+WIvHeNZL8CiHalNNuI2fNDxTsWId/j4FEXzod49KStq6W13CgxoHajfJCCCFEy1IUJyX7dpL949fogoJJuPNhgvea27paXkXivWsk+RVCtAmn3U7Omh8o3L0RRVGIvGQaQQNHyxPfFqa42QxKOU+bQQkhhPCM4r3byf1lEY6KMvz7DSHismtQ6w1ARVtXzatIvHeNJL9CiFalOOyUHNlD3paVWAtyCB1xCf49BmIIkcEuPEGaQQkhhGhtiqJQmZ5M4abVlB3Zj1+fQQQOGoFPQme5ye0hEu9dI8mvEKJVKE4nJYd2kbN+CbbiAnwTuhJz2Y0YI2LaumpezaGocShuNIM6Tye9F0II0TIqT6WSu+IHKlNPoAsJI+rqm/HvI9MYeZrEe9dI8iuE8KiKgiqOLk3j+HvLsRXl49elD3FX344xLKqtq3ZecKLC6UYfICfnaTQUQgjRbHaLg1MbTnHwy8Nk7shCHxZJzIzb8e3WE5XK9Rgkmk/ivWsk+RVCeETKxkx2f3aM1M1ZAPh16Ufs1Jn4RMW1cc3OL9IMSgghhKcUp5Ww98N9pKxMxVpmI6xPKNHX3Yq5e19Uakl6W5PEe9dI8ivEeabQ6doAEy/kjXCp3HfrhtZ5rzgcFHz9HaUbNqFPiCPo6qvwHdCfTut8IBfItTW5zyUHn3Hp2KJp7jeDOj/vBAshhDfJcZS5VO6VfNfm7gVYvrVPnfdlW3eS/+kiNH5+mMdcSOiQAegiI4hfqsBeJ+A8+/7W/tXlY4umSbx3jSS/QogWYzl1mqLFP1F55BghM67FPHL47wa2OF8H1RdCCCG8h6O4hJIVaylZsQbfEUMInjENtV7f1tUSwiWS/AohzonidFJ5+AglK9dSdfQYmuAgwu+ajalXzya3tVrLKClMxS8gDoPRvxVqe/6p7gPketMmd8oKIYQ4PyiKgjUji9JV6yjbugOVWkPg1Evxn3Rxk6M3O50OCnMOYzSFYPKLkNGePUTivWsk+RVCNEvG3gLWv32QtO0/oFgs6ONjCZt1M6b+fVFpNPXKlxSnk5d9gLKS08QmjiEgKIm9W/9LeVl1n2CTbxghEX0ICunKyZMn6dSpU2ufkldyujnp/fk6AIYQQoj6ynIrWffmQU6uz6IstwpNgD+BUybhN2oEapNPvfL2wiLSj+2gpDCNgJBOxHYey8n9i8lM2QiAzmAmKKwb4bGD2LZtG0OHDq23D9E8Eu9dI8mvEMItljIbq1/ex+5vkgnvFkDAxPEYu3TGkBjf4N1ce0Ehp/7vaVJ+t8zPP5aUo0upqMij39A7sVnLKco/TvrJVaSfXEXnzu9is9nQauUSda6kD5AQQgh3KYrCzi9OsPb1A2j1GvpemcBJ4yh8enZD1UBsVhwOsl//L5ZjJ2qXOewWKstyyUrdQlKvy/ENiKYo9zi5p3eRc2onw4a9x+bNmxk+fHhrnprXknjvGvllKYRwy4oX9nJ4aTqXPNqfgTM689X6sw+WUXXiZJ33fv6xZJ7ahs1aisEYhMk3HEOIP+FR/SkuTKaiPJfFixdL4ttCnKhl6gMhhBBuObz0FMv/vYf+1yRx0UN9MPrryVrfq9Hy9sKiOomvRmtAb/QnK3ULAH5B8QSEJBEU1hWAU8dX8cQTTzB48GDPnsh5ROK9a+TXpRDCLcWnyjGHGQmINlFZZGmyvHnoYHwHDSD+JxtlZVmgwK7NbwBgqSpi/84FdOt9NSePLqGiPJc+g2YxZcoUT5/GecOhqHAobkx94EZZIYQQ3qnodDkAcYNDKc+vwuCnO2t5XWgI8a/9G1QqQr/OQmcws3X5swCo1VoObJlPnxG3k3NqF1mpW4hOGsU//vEPj5/H+UTivWsk+RVCuGXE7d1Z9NhWvvnDJgB00Tvx6dYFbWgozvJyHOUVOKuqassrNhuKzU6Q74UEBCWgKE76Db0TX79ILJXF7Nr8Jjs3vY6PKZReA24hJLzpgbKE6xxu9gFynKd3goUQQvym7xUJHPwxnR/+sg0Ac5gRpVMq+vg4FKsFZ1k5jvJKUKqnM1IcTpSqKnz69CQhoHqqxAFj7kOt1qL3CWD32jfYs/4tNBo9CT0mEd1pdJudm7eSeO8aSX6FOM8EqU0ulZsWsKPhFZPhoUlR5GY52LW1ihXr7BzctIWi9TbMQVoCgrQYzdraMQS1BhVH95VyuIeNiHtuA1RAZ4oB8CVi4r3YcwowDexFuU5LOQq9H3/ZpToGnDj7HII1/H7a61K5pWUfulSuI3Eqapxu9AFynqd9gIQQwpuEa8wulZseuLXhFYFw28oASor92LfTwtYNFjasP0jqNzsw+WvwD9Li669Bo62O9mo1ZGRbKDp0iPQFA1Gp1UB47e5CL5tN5d7jmAZ0QxVgJhPo/ZiLsT7ZtVjvv+KIS+UAfs571+WyHYXEe9dI8iuEcJtarSIiWsulV5lJvCwKqB4co6EBrxRF4cmr9pJnaLjJlCExFkNibKPHUhQn1sI89IEhqNT1R5FuDc899xwvv/wy8+bNY9q0aW1Sh+aSO8FCCCGayz9AzahxPowa58M0W0ijsR7g4+cz+PnTPGhgvTbIH78LB531WNbiAjRGExqDsUXq7q5NmzYxZcoUbr/9dp577rkONyWTxHvXSPIrhGgRjQWJ/AwraYcqCLt/IAD2ohLKVm+hfOMu7LkFAAROv4yAyWMb3n7LKnJW/whqNb6J3fCN64whLBKN0UTViQxKC1IpL8zA6BdKQr8pGH2DW/ScdDodNpsNgBtvvJHVq1d3qJEpnbjXr8e1++tCCCHOR2dLCLcuK2bslUEcU6lwWm2Ub9xDyYqt2E7nolis+AzoRuSfbm1w28qsUyR/+BIAPlHxmOI74xMZh9YvEFtaPqX5aZQVpKFSa4jtdQmBEd1a7Jyuu+46vv76a/R6PVarlRdeeIGIiAgeeeSRFjtGa5B47xpJfoUQHpWTWt3/VxcbiS0zh4y/vFSvjKFLQqPbG0Krnyz7de6F024jd+MyFJu1eqVKjW9AFObgOIqyDrN7yXOEJQ4hpsc4jObQc6p3UVERADabjWeeeYY//OEPTJw4kSuuuIJDhw4RHNxySbYnuT/6o+tlhRBCCACHXSHnlJWEbj4cdTjIfPJtrGlZdcoYOsc1ur0hpLqJtNYcgC4whOIDO8jfsuq39b4hmEPisZQXcGjNuwREdCWm53j8wzqf8xPaFStWADBhwgQ+/vhjXnjhBf70pz8xYMAAxo8ff077bk0S710jya8QwqPSj1ag0arQhgRhy8iut948bjjY7diLStAG+tdZV5WTUTufYNCgUZiTuqM4ndjLS3FUlBFaFIJGqwfAYasi68QmMo+uISdlG0kDriSyy6hm1zswMJDDhw8zceJE3nzzTaZOncoTTzzBpEmTOHLkCBdccEGz992a3J/37/wMhkIIIZovI9mC3aYQlWgAp4LTaquzXhcTjqFTDNaMXPTRYXXW2UqLsJeXogsMwa9rXyIvvgJFUXBWVWArLSY434zOWN2HWXE6KTi9j1OHVnBw9duExPaj64ibf+1j3DynTp3i6quv5pdffuGnn37i73//Oy+99BIbNmzoUMmvxHvXSPIrhPCofesK6TbEj4LkUziKSzD27krVgWOE/WEm5Rt3U7ZqC2WrqucBjH//GVRqNYrTQfo38yk7cah2P2q9AQCVWo3OLwCdXwCast8a7Wh0RmJ6jCOy62hSdn9Pyp5FBEb1wOgb0uy6d+/enU2bNnHJJZdwyy23MGjQIBITExkyZEiz9ymEEEJ4m11rS9DqVASEaqlYfgT/8cMo+HQJwTdfhqOwhOIf15P9wkcARP/zntqnwHmbV5Cz5sfa/WhqYr1KhcbHF42PL7ry32K9Sq0mJK4/wbH9yEvbyfEtnxOYuoPwpKHNrrvJZGLx4sXMnj2bWbNmkZOTg8Vi4brrrmv2PkX7JcmvEMKjIuKNrPg0GzbPq11mHjcC04BeKFVWKrbtRRPoT+B1l/6a+DrJXbecshOHUGm1KHY78FuTqKZoNDoS+0+lKPMQh9d/QJeh0/E7h/pHR0fTu3dvvv76awIDAykvL0enO/t8h+2JExVO3OkD1LEG+BBCCNH2ohIM2G0Kj0w5AlSPuqyLCsV/4gjsuYWUrNgGTifmCwehT4oBoCz5CDlrf0JjMuOoKAPAEBrp0vFUKhVhCYMpzDhIyu5FoAI/xa/ZTaB1Oh0XX3wxn376KUeOVNffbHZtxOz2QuK9ayT5FUI0aGgjozOfqZ8+86zrJ/xVz7yoQKJ7qBg4VM+pNDtHjxziy9d3kLrTxtRpRp5+wcTxoytZu+onvvyskrzTDoDaxHfCRAPv//GNevvWnOXCffCgjofn5nFg1WvkXTmYmOtHoA/yPWtde/xf/WkXinZsIvuHr1HpDRwOjCI3dzXxs+8n7YP69WmPpBmUEEKIxgzQuz6ychdtfqPrLr5cIfzZIBwOhYun6snLdZCebufrz59mxXILCbEavlsUQlHRCbZsfoH/fVHJzh3VTaNrEl//ABWL/v0zZvMvdfbtd5YcrbDQyd+eUFj47f/IHxhP53svwtwprPENftXt6brxvupUKulvvwrAEl31LfMBM28nf9Uv9bZtryTeu0aSXyGERwUFavjL3EDS7BYAjh5W+Osfi2vXr/jFwo/fZ+F0gtEIl13hw6KFlfya99Kpk4YXXwxw+7i9eun48YdQ3nu/nKef30Pm9zvx7x1DxOT+hF3Su8FtFKeTqow0LFkZ+MR3Qh8WQfYPXwEQe+Md+MQnUbRtAzk/f4fD8SoaTdtMveQO96c+OD+DoRBCiOZTq1XcPrP6SWmB04bZrOKKy/OxVId+UlMcDBucg/XX8SpHj9EzeKiOHduqE2CdHv77YTBms3sxKChIzZuvB3HN1VXc/qcidtz1Eab4YELHdCPhpuGo9Q2nOta8HKrSUtGYzZi69iDvl58ACJlwGYbIaIIuvITC9atISUkhMTHR/Q+kDUi8d40kv0KIVjVwsJ6H/mSmIN+Jr1mN2azC7Kema3ctPXvr8PFRkRCrYe1aC4MG6bjzLl8Cg5p3gdZqVdxzt5mf+91KwabjZP+4h2PP/0jxvnQSZo9FF2iqLVt5upDUdz/Gkv3rk2yVCp+4JAB8u/bClNAZgPBLp5H2/mu8+uqrzJ0799w+jFbgVFQ43Zn6wI2yQgghREP0ehUvvBjAtm02/MwqfH99JSZo6dNPR0iImp9/sWCzldGth5brrjcxcLC+2ce7eJyRofOnU7gzldy1x0j7dDOF25LpNnci5i6/dZtyWu0cfv5nclcfqV1miIrBkpWBSqcjYFj1QJnBY8dTums799xzD0uWLOkQc/5KvHeNJL9CiFal16u454Gz98J9eK6Zh+e2XF8bXYCJiEv7ET6pL1nf7STtw3XkLN1H4JAkejw5DbVOQ8bXW7GXlBB36xyM0XEU79pKRepxQsdNJnDob6NG+8QmEDR8DH/961+ZNm0aSUlJLVZPT3C6eSf4fJ36QAghRMuaeqUPU6/0aXT9+IlGxk90vdl1U9R6LSEjOhMyojMRE3px7LXl7LjnY3zigujzr2mYYoPI33yS3NVHCL/yOsx9B1CVnkrJji34JHbCf+gFaHyq66s2GAi/8lqWfvwen3zyCbfcckuL1dNTJN675vw8ayHEecdpd2ArKCPyqkEMWnAX0dcMpWjrSSqScwFQaTVo/PwxJXZBrTcQNHwMMdNnEzJ2AhofU519hY6/DLPZzBtvtP9+v05F7fZLCCGE6KgseaUE9otlyLu30vMvl1GVUUze2qMAqHTV3ZV8e/RGY/TBt2sPoq6/lbDLp2EIrzvYlm/3Xlx55ZW88MILrX4OzSHx3jXy5FcIcV5I/e9qMr/dgUqrJmR0Nwq3nsQnNhifuGAAjNGB2PL34LBUoTGc/U60Wqdn2rRpLF26lBdffLE1qt9sDlQ43BjR0Z2yQgghRHuSv/kk+//2LQAhIztTkVaASqMmaGgiAD7RgQBUnU7H3KPh8T9+76abbmL69OmcPn2amJgYT1W7RUi8d835mfILIc47oRf3AkCxO8lbfZjAwUn0eeUmND7VfYx84kJQHA6cVZUu7W/48OEcPHiQykrXyrcVuRMshBDifBHQJxq1sfrZXv7GEyh2BwPfuBG/rhEAmGKDAHCUlbq0v+HDhwOwa9cuD9S2ZUm8d835edZCiPOOX/cohn79AGET+6DSqincegKV5rdLoL2kOok9s4lzY0JCQlAUhfLyco/Ut6U4+O1usGsvIYQQomPSmo2M/v4BOt01Fq2fkaqsEiy5vyW69rIqADQm12J9aGgoACUlJS1f2RYm8d410uxZCHFODCrX5gPuqnOtnDvS7K7duZ2dsPG3N68Hsn9dT1694wBXmtcTHl89uMV3xamkBRkZcsVxl0Z1/Pt9+wCYet1LGIyBjZZbveRRl+roKe7e3T1f7wQLIYQ4O7PatcGpXC3nqhQXY/19iat+e/Mo2B/qwS39djPCvp2JiakAHNpWxkag54RyArsda3KfQ//+FgB/+mgRz+zKbrTc/hcedqmOniTx3jXn51kLIc5r5UXV8wpq9b9dAo/vKCG0T7jL0xlUVRYCKrRa1+4eCyGEEKL1lJU4cDpBq/8trh/eXobBpMa/U7BL+7AWFwCgNbXcDBSe4lDUbr/c9dZbb5GUlITRaGTw4MGsW7furOU//fRT+vfvj8lkIioqitmzZ5Ofn9/cU2wRkvwKIc4rOWmVfPjX43Qe6EdQRHV/36PbijmytZj4i12btqjgSB7JR34iJKwHGm3z5yVsDa0RDME7AqIQQgjvoCgKz8w+jlanYuCFAQCUFdtZ8lEOwyYEotY2HescFjvp381H4+OLKa6Lp6vc7v3vf//joYce4q9//Su7du1izJgxTJ48mbS0tAbLr1+/npkzZ3L77bdz4MABvvrqK7Zt28Ydd9zRyjWvS5JfIcR5RatTY7M4KcqxcmhTMYqi8OW/k+k80I+EiZ1d2kfKz8dRq3V07zfdw7U9dwoqnG68lGaM/ugtAVEIIYR3UKlUqFRgsyhsW1aEzeJk8XvZWCqd3PjHaJf2kbc7A0t+NnFTZ6LRGzxc43Pn6Xj/0ksvcfvtt3PHHXfQs2dPXnnlFeLi4pg3b16D5Tdv3kxiYiIPPvggSUlJjB49mrvvvpvt27e3xOk2myS/QojzSnCUgb9/O4CAMD0vz97Ph389zumj5QyZHIpa49olsTK/EpM5Ap2u/Td5bo0nv94SEIUQQniPv37QhYuuCea9J9N59IpDnNhXQc+hZkKiXGuxVZVfAYBPjGutwtpac+N9SUlJnZfFYqm3b6vVyo4dO5g4cWKd5RMnTmTjxo31ygOMHDmSU6dO8dNPP6EoCtnZ2Xz99ddcfvnlLX/ybpDkVwhx3onvZeb+edVTHxVkVKHRqkBxfXtbuRWNtv3fBQZwKiq3X+BaMATvCohCCCG8h3+IjjnPJRIarSczxYJaA4qbsV6l0aJSazxXyRbU3HgfFxdHQEBA7evZZ5+tt++8vDwcDgcRERF1lkdERJCVldVgfUaOHMmnn37KjBkz0Ov1REZGEhgYyOuvv97yJ+8GSX6FEOelvauqB7G45V9d0ejU2KxOl7c1R/lRUZaFori+TVtxoHb7Ba4FQ/CugCiEEMK75GdaycuwctuTcfj4arBWuR63faP8URx2rIW5Hqxhy2luvE9PT6e4uLj29fjjjzd6jDMHBVUUpdGBQg8ePMiDDz7IE088wY4dO/j5559JTk7mnnvuabmTbgZJfoUQ551Vn2aw4C/H6DzQj9BYAwm9fPn5vdMUHnNtwKXES7tQVVlI1ukdHq7puWvunWB3giF4R0AUQgjhPU7sK+ePlx/CL0jDwAv9Sept4sDmMtZ9X+DS9mGDYtD6+pOz4WcP17RlNDfe+/v713kZDPVbtoWGhqLRaOrd1M7Jyal387vGs88+y6hRo/jTn/5Ev379mDRpEm+99Rbz588nMzOz5T8AF8k8v0KIDmtLVaxL5Z75aRoA9pISipb+QunGk/iPHYPjiqk8u0RDSVQ4FZu+ZfULBwl/eHbTO9R0wdzpOKcKd6C7dOS5nILHOVHjdOM+Z03ZmiDYlHMNiAD9+vXD19eXMWPG8NRTTxEVFeVyfYUQQni3bS7G+seWXA+AYrNTsn4jRUt+QR8ZS/jdt/PCHhN2/xLgn7z+SArx8+NQqZuOjf4TL6bg+0WUBVeiNrbs/MUtrbnx3hV6vZ7BgwezbNkypk2bVrt82bJlXHnllQ1uU1FRgVZbN9XUaKqbkCvutD9vYfLkVwhxXnBaLGS8+DLlu3cTfNWVBF91JapfL8K+AwegCQykcu8Rir79BcXZdLMoY1gUVXmZVOU13LS3vXAoKrdf7vh9QPy9ZcuWMXJkwzcGKioqUJ/xo6M9BEQhhBAdX+7Hn1H43WJ8B/Un4t470PhWD06p8ffDd/BAAHJeWoCzvLLJfemjosDppHzvXo/WuSV4Ot7PnTuX9957j/nz53Po0CEefvhh0tLSalttPf7448ycObO2/NSpU1m4cCHz5s3j5MmTbNiwgQcffJBhw4YRHe3aiNueIE9+hRDnhdItW3GUlRH7+GPoQkPqrNOYzcT9/a8UbVxO0cJfUCw2gq4/++BLQX1HUHJsHylfvE7XO/+OxtA+7wj/vmmTq+XdNXfuXG655RaGDBnCBRdcwLvvvlsvIJ4+fZqPPvoIqA6Id955J/PmzWPSpElkZmby0EMPtXlAFEII0bFZs3Oo2L2XkOuvw2/k8DrrVCoVYbfehHnEUHIXfEz2i/OJ+POdqA2Nj/5sTErC1LcveV/8D21QED5du3r6FJrN0/F+xowZ5Ofn889//pPMzEz69OnDTz/9REJCAgCZmZl1pjicNWsWpaWlvPHGGzzyyCMEBgZy8cUX89xzz7l13JYmya8QwutZs7MpWvIz5kGD6iW+NVRqNQFTxqHSaSn84kdMQ/pg6JLQ6D71gSHEXn4zJz9+CUteJqZ2OhWCoqhxujF9kdKMqY68JSAKIYTouJxWG3kff44mKBDz0MGNlvPp3o3wP91O1lPzKPl5HYFXjm+0rEqjIXzWraQ9+X9UnTjRrpPf1oj3c+bMYc6cOQ2uW7BgQb1lDzzwAA888IDbx/EkafYshPB6OR9+hCYggJBrpjVZ1m/CKHTxURR+s7TJsvrAEFRaPXlbV7ZENT3CgcrtV3PMmTOHlJQULBYLO3bsYOzYsbXrFixYwOrVq+uUf+CBBzhw4AAVFRVkZGTwySefEBMTcy6nKoQQ4jxW9OMSbJlZhN8+C5Xu7M/3DImx+F8ykpKf1uAorzhrWZVKhT4qkpL1G3A2Mu1fe9Ba8b6jk+RXCOHVrBYntswsAi4e59JgFSq1GtPAXtiz85osqzH4ED7qUsqSD+Owtt+AKIQQQng7S1o6pn59MMS7NkCWz+DeKBYrjqLSJsuGXHklzooKqk6cPNdqijYmya8Qwqsd2lod1HRhoS5v46ysgkam6TmTX+deKE4HFadONKt+nuZU3J3+oK1rLIQQQrinrMiOLSMLrRuxXqmsvmnd2LR8v6eLikITFETlkSPNrqOnSbx3jSS/QgivVVXh4P0nUjF06oQhofH+u7/nrKyibO12TEP6uFReHxSGzi+IspT2GRCdv/YBcuclhBBCdCQfPp2OgoLfqBEub1OydD262Ei0kU0nzCqVCp9u3ag8evRcqulREu9dIwNeCSHanRJn09MPAHyWNfGs69OXHiHnlJWe18/AeFTT5P4qJloo3r4bxWol7NoR6Pwbb8psCakZHVKFT49ulKYeJbjhsbTalBMVTjf69bhTVgghhGiuQufZ+9rW+CRz0lnXWwoqWPfddqLHXUNQfhDkN73Pil7ZVB04RtSD0zAFWhstV1XyW6rk06sbZVu2YCstRBsU6FLdW5PEe9ecnym/EOK8UHI8D1OUH8aAMJe3qTyShiE+Al2Iv8vbmLp2x5aTjb24qBm19CxPz/snhBBCtKWSE9VjdJjju7m8TeXRUwD4Dujs8jbGbl1BpaLySPt8+ivx3jWS/AohvFZlThk+ka4nsQCKUwG1ewHBp3N1QCw/etit7VqDNIMSQgjhzSqzywDQ+Qe5vpHTCYBK43rM0/ia0MfHUnmw/cV6kHjvqvPzrIUQ5wWfcDOVOU2P4vh7pt6JWE5mYsstcnkbjckXn6TOlO3Z5WYNPc+JO4NfuNdkSgghhGhrPuFmAOxlxa5v0zMBVFC61b1E1tSvL5UHDuGsqnJru9Yg8d41kvwKIbyWb0wAlVmlKA6Hy9v4De8JQMW+ZLeO5TdwMJUnj2PJznJrO09Tfu0D5OpLOU+DoRBCiI7JFBsAgKWo6SkKa+hC/PHpFkf5HvemLjIPHohit1O2ZZtb27UGifeukeRXCOG1fGMDUBwK+Ue2uLyN2seAyqjHUe7aoFs1/AYMRhccSt6ib1CU9jN/gHvTHlS/hBBCiI7CJ9wPlUZN4f7NKE7Xb3ZrAn1xlrv3BFcbHIR5xDAKf1qKo9S9lmWeJvHeNZL8CiG8lim6ur9v3qHN7m3ocKLSND069O+ptFpCr5hG5ckTlO3d7d7xPEj6AAkhhPBuCorDSfGxPdgrXRtBGvg11rsf84KmXoZKpaJw0U9ub+tJEu9dI1MdCSHaHR+VzqVyBzIiz7o+49lf0AT40qfvTfictje5v5IT1f2GFIcTZ4EP9l/fN8Th66y3zDCwG6adfclb8j3GYb1RabVM7vl4k8e1OxqfUulcuXt393y9EyyEEKJ1aVxsdns4K+Ks64t+2AhA91GzCSryhaL68flMOUf9cZSoUet02I42PjCmEm6rt0xtNhJ4zaUUfPwtfpdegD4hhqQ3XmzymM5Kz/YTlnjvmvMz5RdCnBdUahVqkxEfX9cn4FUUBZxOVOrmXR4DL5uEo7iEin0HmrV9S3On/4+7cwQKIYQQbe7XGRpM/me/IV6P0wlutvKqYb5wGJrgAEpXudmyzIMk3rtGkl8hhNfyHdITW2Y+itL0XeBav05/0NyAqI+KxNApkZK1G9pV318hhBDCG/kOrR6osrIs163tFKej2Te6VWo15jFDKd+yG0dZebP2IdqGJL9CCK9lLyxFZdS7tY2jorq/kFrv3na/FzB+HJYTJ7EkpzZ7Hy1FBsAQQgjhzRyF1QNPabQGt7ZzVlSgOodY7zfuAgBKl29s9j5aksR710jyK4TwWuVbDuA7uDsqleuXusrjxwAwJiS6VF6x2XGUlGLLycVeVIyiKPj07okmIIDyXXuaU+0WJcFQCCGENyvbcgCNvy9+IQkub2MvK8WanYUxPtGl8orDgaOsAltOPracfBSHA42/GdOQvlRs39vMmrcsifeukQGvhBBeqWL3Maxp2QTPGA+H3Nju4AEMsXFoAwIaL3P0CIXr12BNS8NZUXdKJLWfH6Z+vdHHRFGxbz/OsBGoVc1rQt0SZAAMIYQQ3spRUk7Jih34jenv1o3uisPVPwx8e/ZqtIw1N4eCxcuoOnQcR0FxnXUqvQ5jzy5oI0KwZeRgzchEHx3VvJNoIRLvXSPJrxDCKxX9tBFD5xhMg3u4lfxWnUrHt2fvRteXbN9K7pf/Q58Qj/+4C9EGBaL28UFlNKBUWahKTqFsy3acv87/l6LZRqeQEed6Os0mwVAIIYS3Klm7G6fFSvB142Cp69tZTqWhCwtH49vwrA7WnGxOvfkKapMR3+ED0EaGojb5oPYxAgrWtEzKt+ymck/1D4yCbxYRcd+dze5D3BIk3rtGkl8hhNdRHA4q954g8IrRqFSuX9ztRYXY8/MxxMU1vF9FoWDpz5gHDCRk9o0N7tvUtzcB4y4k+935WFPTOJq7GoCk4GFu3ZVuKQq4NaKjDNElhBCio6jYdQxDfAQaf1+Xt1EUhcqTJzDGxTdapmjNKjQmX6Keegi1T/2+xD69u+E/aQyFny2idOUmqo4eI+ed+YTOvBGNr6lZ53KuJN67RpJfIUS7c9pR2XQhgNRGAozdDopC5c4UGGUCZ9Nz/AKU/7Ietc5ASGhvNNn1A0hFVjqO4mJCuwyn+z+PnnVf3ZXxHDNuI7VqP0dzV+OfUMaaNWswmerXuaSkhICAl12qo7vkTrAQQoj2KMPh2kwMjlONJ5PW1FwcJSVYj4DW4tr+CnOOYsvJJuqiqzAU1l+vOOxUHDpIYJ+hGAsafjJcI+KiazEFdyL760+pPHiY9Mee4MCBA/TqVb85dUlJCQGP/s2lOjaHxHvXyIBXQgivk//zEtBoCJ16pcvbWC2l5O/eQMjgsWj0xgbL5G1bjc4/GN+4zk3uT6PS0sP3ApYvXw7A9u3bWbRokcv1aSkyAIYQQghvVHH4MI6SEoIuu8ytp62pR5bhExWPb2K3BtcX7d+Oo6KMwL5Dm9yXSqXCv/9gUlNTCQ8PB+Dpp592uS4tSeK9ayT5FUJ4nbI9uwgYOQpjYpLL21SW56PYbfhENtwMylKQQ8nRPYQNuxiV2vUBrMaPH8/rr78OwOnTp13erqVIMBRCCOGNynftRhcZScD4i13eRlEUyksy8ImOb7DrkuJ0kr9lJX7d+mIIjXR5v/Hx8axduxaAVatWubxdS5J47xpp9iyE8Cq2wkIcxcUYkzq5tZ1/UDym6EQyVyzENyYJjbHuXeTcbSvR+voR2LvpO8Fnuu2229i7dy/jxo1ze9tzJc2ghBBCeBtFUbCkpmDs0sWtsT1UKhWxnS8kbedK/HsMwBRb97dCyZE9WAvziJl6i9t16t69Oy+88AJBQUFub9sSJN67Rp78CiG8hmK3k7/4e9RGIz6dmm6a/HsqlZrYy2/BYa0ka+0PddZZSwopOrid0CEXodbq3K6XyWTi3XffZdCgQW5ve64UReX2SwghhGjPStavx5aTi6lvX7e3je82Hp/oBDJ++h+K47cxQRRFIW/TcnwTu+MT1fDAl0354x//yO23396sbc+VxHvXSPIrhPAKVSnJnHrjVSoOHyLsuhloGhhYqil6/yDCR15K4f4tVOVn1y7P274Kjd5IUL8LWrLKQgghhHCDvaiYnI8+puDb7/AfMwZT9+5u70Ol1hA16RqsRXkU7tlcu7zsxEEsuZmEjrykJavcapyo3H6djyT5FUJ0eAW/LOX0W2+gUqmJmXM/5r79mr2v4H4jQaWiPP04APbKcgr3bSFk4Bg0+vrTHbR3EgyFEEJ4g4ojRzj13HNUHj9O2I03EjLtqmbvyxgWjW98F8pTj9Uuy9+6Cp+YxHpNoYV3kT6/QogOy2m1kPf9d5Ru20rQxEsJunj8OU8wr9ZqMQSGYfn1yW/xoR2gOAkeMLIlqtzqpA+QEEKIjkxRFEo3bqLg++8xdu1K2M03ofHxOef9GkIjKUs+DIAlL5uK9JPEXnmrW32I2xOJ966R5FcI0SoqFYvLZR9JvbrJMjlb03C8+zaOvDzmz5/P7NmzGy/8J9eO2/ePL6MoThRFqe0HVJlzGmNYNFqTX52yP+e969pO25i7/XrO1z5AQgghWkaZs8qlcv86fXnT+8ooIf77Raxdu5b77ruP//znPxiNDU9HyMOu13HwXS+jqXSC1Y6xACpOVs/GEBzQDW3Bb+UO/NuNnbYxifeukeRXCNHhFB7OYfNjSxg35iJWrFhBly5dWmzf+TvWYi3MIXrCtQA4KsvrJb4didwJFkII0RFZy6ysefhnQtRB/PLLL0yYMKHF9l2Rm07uoU1EDhgPgL2qDJVag9Zw7k+U24rEe9dI8iuE6FCq8ivY8vgSArqE8sMPPzR+B7gZ7JXl5GxcSsjAMZjjqhNqra8/5enHUJwOt+b3bS/kTrAQQoiORnEqbH5yJVUFlSzdsZVu3bq16P5PbVmEMTCcqEETAdCZ/FGcDqqKczEGhLXosVqLxHvXyIBXQogOQ1EUdj23CsWpMPyZS1s08YXq/r2Kw07YiN9GegzufwHWogLydqxp0WO1FsXNCe/P12AohBCi/Tj29QEyNqYz8p8Xt3jie/jwYcoyTxA1aFLtTe2A+F7ozUGkb1iIojhb9HitReK9ayT5FUJ0GOk/HyF7YyoD/nQhxlDfFt+/025HpdXVaebsExFH6JALyV73I9kbluCwuNaXqb1QAEVx49XWFRZCCHFeKz1Vwp552+h6TS+iLmjefLtnU1VVHcf15sDaZWqtnrjR11J6+ijJyz+kqji3xY/raRLvXSPJrxCi3VMUhRNf72XXv1cTO6kbUaOTPHIcnV8gTmsV9oqyOssjxkwhdMg48ravIeWbd1GcDo8c3xNkqiMhhBAdRe6eLFbcswhjiA/95gzzyDHi4qoTaktJXp3lAXE9Sbr4Fspz0zjy3StYSgsa2rzdknjvGkl+hRDtmr3Kxo5/LWffK+vpdE0fBj0+zmPH8o3rDEBZ2rE6y1VqNZFjp5B03T1UZqVSuG+Lx+rQ0mr6ALnzEkIIIVqToigc/Wo/K+/7Ab+4AC555wp0Jp1HjhUSEoIxKJLS08fqrQvqPIBe1z6KRm/k9ObvPXJ8T5F47xoZ8EoI0W5Zi6tY/4fvKT9VzJAnJxA7oatHj6c1+aHS6rCVFja43hSdiF+nXhTs2+zRerQkp6JCJaM/CiGEaKcURWHbs+s4ufgI3Wb0YcD9w1FrPft8zuAXgrW8qMF1Gr0PEf3Gkb7pO7Kzs4mIiPBoXVqKxHvXSPIrhGgVrxX2drls5ludAEje+jUVaZX0Gv8gmmPRZJ55k3aUa/ubOOKfLpUrjT2FYrdhiutMYzEhsO9w0r+bz/DI6QQbY5rc55KUl12rpIfU9O1xp7wQQgjRXC/kD3ap3Il5PQAoSN3DyTVHSBw5gwDDMJL/e0bBEa4d9+IJ/3apnN1eRWnmMcIGXYy9kXEz/XoPRLVlERcMnkHn+EsaLvQ7yzb+zbVKepDEe9dI8iuEaJcqCjPITd5OTK/xmIKiW+WYBTvXYYyIxScyvtEyfp17YYyIY3fuj4T5JOGnDyHBbwAqlfQiEUIIIdzhsFk4vXsp5vAkwrp4po/vmTIztuN02Anu3XhWrTX6EjpgLMk7VlFpKcSgDyAhehR6XcsPtilalyS/Qoh2KWX7t/j4hRHV86JWOV5FVT5lyYeJufQGVKrqx76K00nBrvUU7tuCPiCY6Ekz0JrMRE+aTv4Xn1Bqy+N0+QG0agOxZtefbLcmmfdPCCFEe5V9aC3WsgK6XDizVY6nKAqn0zcR2GUAOl//2uWlaUfI2bYMp91G9IXT8I1MJHzIBJQTp6iozCen4BBVlkL6dpvRKvVsDon3rpHkVwjR7lgrSyjNSyFp2LWotZ4Z8OJMhaVpAPh361e7LHvdj+RvW41/9/6Upx/n6Nv/QONjQmvyI0DrT7Rvd1JKdpFdcVySXyGEEMINitNJYdp+AmJ74RMY2SrHtFpKqKwsILzrVbXLSlMPk7zov5iiEgE48dXraIwmtEZfzAQSFtKTUKUHqafX4XTaUavbZ/ok8d417fOvJ4Q4b+WllLNvyUtodEYCo3u22nErKvPQ+QWi1hsAqMw5Tf621USMvZzQYRdjLS6g9MR+HFWV2MtLqDp0kt25PxLp252cipMoirNdNn2WATCEEEK0Nw6bk8NL36Si4DTR/Se02nErKqqnNzIEhgHgdNg5teorzPHdSLriThS7naKjO7FXlGGvKoOULE6krSAqbAAOp5WS8gwC/RrvGtWWJN67RpJfIUS7suKtY6jVGnpf+jB6H/+mN2ghWq0Re2UFTpsVtU5P/rZV6AODCRlyIQD6gGBCBo2tLR9deJJlaW9isZfhUKxUOcrw0bZefV0lA2AIIYRob/b/lE5ZbgrdJ9yNf1S3VjuuVusDgL28BIIjKDq6C1tpEUlX3IVKpUal09fpCxzuU86OA/OpshQBUF6R026TX4n3rml/jymEEOetA8uz2Lskk6heF6M3BbTqscOCeqDYrZSnHcdhraLk2F4C+45ApdY0WF6j0hJsiKHAcgo/XShGjblV6+uq6mDozrx/bV1jIYQQ3qwgrYzVrx8iMLZ3qya+AGa/KAyGAEqS9wNQeHgb5tguGIMbn84oOLAzhSXJ1f8O6Nwq9WwOifeukSe/QohzcthW7lK5/+4efdb1ZRv2kPv2UnyH9aV7ahTqjJQWqF01Zeu+JsuYFAWj2oxqyRa02mModjs99wfgc+hIg+WX5L7NvfdW8fbbb/PC609x9913t1h9W5L0ARJCCNES9lkrXSqXaWn85nXhiUKW3LcBvVmP321XkxfYchmYbv0Bl8qF2sMoPnCQwJwulGcep0/QJUR/l9pg2SVpr7By5UjGj19O//79Wb/zxRarb0uTeO8aefIrhGhzpau3kzvvK8yjBxB233TUbdB3VqVSEaqLJbVqH/vL1hChT8JH43fWbXr2rO6TPHXq1NaoYrMozXgJIYQQLS3/SAE/3r0Un2Ajl797KdrA1m3hVSNEHUWZPZ+1mR9g0PgSaep61vLx8dXNnB988MHWqF6zSbx3jTz5FUK0qYrdR8h77zv8Lh5KyKypqNRtd0+um+9wyh3FWJVKepvHNFn+/vvvZ/bs2fj5nT1JbktyJ1gIIURbK8sqZ9ncFfhFm7n09QkYAgxtVpcIdTyJ5oGkl++nf/ClaNX6s5bv0qULRUVFBAS0TbLuKon3rpHkVwjRJpxWG4VfLqPk54349O/a5okvgF5tZFjAVEBxaeRmtVrdrhNfwP3bu+frrWAhhBAtTlEUji85yeYXtqDz1XPJC+PaNPGF6pZePYLG0j1wtMuzNLT3xBeQeO8iafYshGh19sISMv/xLqXLtxB8w6VEPHJLmye+NVQqVbucsqjZ3Br8QgXn6Z1gIYQQLctpd7L+6U2sfXI9cWPimPbZVHwjfNu6WrW8KtZDq8T7t956i6SkJIxGI4MHD2bdunVnLW+xWPjrX/9KQkICBoOBzp07M3/+/OaeYYuQJ79CiFZlzcgl+7kFKA4nUf+4B0NCVKse/7iyj3i6olcZa5fZFTvF5HGicDVatZ6BfhPRqdv2znRLkakPhBBCtDZbhY2Vj6/h9JYMxj45iq5TurTq8U/Zj2FWBxKoDqtdpihOSpUiTtr3UZZZSv/gSQQYIlu1Xp7k6Xj/v//9j4ceeoi33nqLUaNG8c477zB58mQOHjxY2y/6TNOnTyc7O5v333+fLl26kJOTg91ud+/ALczLbnkIIdorRVEoXb2dzCffRmXUE/2Pu9sg8d1PCkdI5WjtMqfiYBNL2cV67IqNYlsOG4u+ociW3ap16+i84W6wEEKIc5ezL5dFs34ke08Ok169pNUT3zxHBgftW9htXVNn+X7bRjZbf6LAmY1TcbAl92vSy/a3at06spdeeonbb7+dO+64g549e/LKK68QFxfHvHnzGiz/888/s2bNGn766ScuueQSEhMTGTZsGCNHjmzlmtclT36FEB6Xnmon69kPqDpwAvOYgQTfcjkaX59Wr4c/QQDkkY2i7CGRHmSTjoVK+nEBUUF9qXSWsad0OVuKF9HHPJYYY/dWr2dLao0BMLzlbrAQQojmq6xw8sZ/Slg8/ydCe4Qw9f3JBHUOavV6+KmDAVBwss+6gXhtD7RoyXSmkKDpSWdtfzShwRwuWsuBwhWUWHPoGXQhapWm1evakpob70tKSuosNxgMGAx1W79ZrVZ27NjBY489Vmf5xIkT2bhxY4P7X7RoEUOGDOH555/n448/xtfXlyuuuIJ//etf+Pi0/m/AGpL8CiEalOMoc6ncZYvnnnW9vaiYrOdfQ1dSwWDTREL3xMCe02fdZklxyz4BVA/uA0CE0pteeaEcTF1MOcU4wwJQqfQYCv0I7zuew/dXD14VYOuN45Pv2LduNWkXawi8amK9fU4Y/bRLx162/q8tdRrN426/nmYkv7+/GwzwyiuvsHTpUubNm8ezzz5br3zN3eCTJ08SHFz9IyUxMdHt4wohhGg9Vy5+qNF1isNB7rsLqDqaS3e/USQU9adirpqKs+zv5OlHWrR+FZf2r/33wJIE9q59g0x7MoXaYpL6XglbIGjMRKyBsWTfXImR6YSv6Ez6f38gL7CM6MdvQm2oO/Lz0CV/cenY2yY/06Ln0izNjPdxcXF1Fj/55JP83//9X51leXl5OBwOIiIi6iyPiIggKyurwd2fPHmS9evXYzQa+fbbb8nLy2POnDkUFBS0aUsvSX6FEB5jSU0n9+0PQKViuO/lGNWmNq2PSqUiJnQQoFBUdoq4sCEAnMrdTnLWemBydTmdluBZ16AND6Hom59RGfQETL6ozep9LprbB8iVO8HgXXeDhRBCuM9RXkHuO/OxpKQRfvdskr5v++u4r38kvUbcRnbqVoIiexIY1gUfcxgn935P3zFzassFjB+MPiqE009/TOYLXxD95xtR6TpmetTceJ+eno6/v3/t8oZifQ2Vqm5yrShKvWU1nE4nKpWKTz/9tHa07Jdeeolrr72WN998s83ivfT5FUJ4hDUji5w330MTHETUn//Q5olvDZVKRWzYEPokXYWfKRI/UyRxYcNIzd6E4nDUKRdw+TgCpo6n6KsllK7a1Ia1PgfNnPU+Li6OgICA2ldDT3Dh3O4G79+/n2+//ZZXXnmFr7/+mvvuu8/l00pOTubYsWP1lh87doyUlBSX9yOEEKL5nBYLuW+/jy07h4iH5uDTu2dbV6lWQGgnug2+nrCY/mi0epL6XkFJQQqlhWl1yvn0SiT6zzdSuT+ZrFe/qfNboENpZrz39/ev82oo+Q0NDUWj0dSL6zk5OfXif42oqChiYmLqTBPVs2dPFEXh1KlTLp2SJ2K9JL9CiBZXvn0XWf95A02AP+H33Y4mwL/pjdpQZHAf7A4LlpPp9dYFXDUBv0tGUfDxdxQtXIridLZBDZvPnWkPft9fKD09neLi4trX448/ftbjNPdu8LBhw7jssst46aWXWLBgAZWVlS6d16xZsxp8srxlyxZmzZrl0j6EEEI0n/V0BpnPv4I1I4vwe+/A2Cmxrat0VkFhXdHqfCjMPlxvnalfZyLnTqds6yEynv4ER5lrsag9aW68d4Ver2fw4MEsW7aszvJly5Y1OoDVqFGjyMjIoKzst250R48eRa1WExsb69JxPRHrJfkVQrSoij37yfvgM3z69iLykfvQmNrHE9+z8TNV37W0Z+fVW6dSqQi6YSqB102m+MdV5Lw8H3tRSb1y7Zqbd4HBtTvB0HZ3g3ft2sWoUaPqLR8xYgS7d+92aR9CCCGax1FSStaLb6DSaol69A8YEhse3LA9Uak1mPwjqSqvH+sBzMN6EvPETKpOZpD26NtUHklrsFy71ox476q5c+fy3nvvMX/+fA4dOsTDDz9MWloa99xzDwCPP/44M2fOrC1/4403EhISwuzZszl48CBr167lT3/6E7fddpvLTZ49Eesl+RVCtAin1Ub59t0ULvwBQ5dOhM66AbXR2PSG7YDNXn2HV+3n2+B6lUpFwOSLCH/4Nmynssh84mUyc3ajdIBJcT15Jxja7m6wSqWitLS03vLi4mIcHbXJmhBCtHOKolB19Dh5n/wPUIh48F50EeFtXS2X2a0V6PTmRteb+nQi/rl70AaZOfX39zn1wSqcFlsr1rD5PB3vZ8yYwSuvvMI///lPBgwYwNq1a/npp59ISEgAIDMzk7S0324YmM1mli1bRlFREUOGDOGmm25i6tSpvPbaay4f0xOxXpJfIcQ5s6SkkfHP58n74FPUviZCbpnRaJPX9qigNBkAXXTDTypr+PTpRtQ/H8bYqysHjn3NroMLKCnLaPZxDxw40GBflhbVzD5A7miLu8Fjxozh2WefrRP8HA4Hzz77LKNHj3b/JIQQQpyVo6KC7NfeJvu1t7FlZBI6+2Y0vu2/dVcNS2UxFWW5mPzPHut1EUHE/vM2Qq4fT/Z32zgw5z0K1h1q9g3v7OxsNm1qhXFDWiHez5kzh5SUFCwWCzt27GDs2LG16xYsWMDq1avrlO/RowfLli2joqKC9PR0XnzxRbcGuvJErO+Yw5kJIdoNW14BOfPmow0JJuKBOzvUHeAayZnrCfXvgi4suMmyGj9fwu65kcDSvhxL+Zmte94iNKg7CTFjCApIdOl4VquVJ598kn//+98MHjyYlStXnuMZnI3q15c75d0zY8YM8vPz+ec//0lmZiZ9+vRx6W7wAw88wJAhQwgJCWH69Ok89dRTLh/z+eefZ+zYsXTv3p0xY8YAsG7dOkpKSjz8eQohxPnHalXIfe9DbBmZhM+5A2PP7h3qJjfA6eNr0GgNhMYMIL+JzE+l0RB89VgiL+xE+n+Xc/LZ7/BJCif8iiGETuzn8rl/+eWXzJgxA4DU1NRzPoez83y8b22eiPWS/ArRjhU6zzZD3m+cbty+22ZxbcL5+35sev49Z2Ulxf9+DYNTz7DQG9H/4gs0PEjEzy08dy/ApcF3ulTu2N8bH31Ssdsp356Lz1UX89LYz10+duqIMBz2/mz/PpP1n6az8+B7DLkiGt8gHT5+WnpdFEZMDz96/a3uXXFbSSGnvl5AVWb14FonMTL0X2+6fFy3uXt3t5ktuefMmcOcOXMaXLdgwYJ6y2ruBjdXr1692Lt3L2+88QZ79uzBx8eHmTNncv/999fOHSyEEN7GpthdKvdjRUDThYC5v9zUZBlFUcj/5Cusx3fSadq9+Go7wVkaLS05/bpLx3ZVz2//4VI5v7stZ11f+udUAkYm4ri/grd7fOPSPtNtITAugsNbjSz9IJNdr/6EefsG4nqY0OrUdB3kR98xgXR75uU62zntdnIXf0PJ9i0AaEy+jHv3Y5eO2WytFO9bkydivSS/QohmURwOcuZ/jN1ayrA+d6LXNdxftr1zVFTfYFA3Y2AujVbN8GtiGHpVNEvfOsH+lbnYq5yUFVpZ8toJ4vv6Y4sPwtylF2qjiYqUY2Qs/hy1TofG5Itapyds3OXYS4pa+KzOD9HR0TzzzDNtXQ0hhPBqJctWU75pG3ETbsQ3plNbV6fZ7CWV+MSHNmvbHsMC6DEsgG1L81nyXga7VhRirXTyw9unCY7SQ88A/PoNRBsUjL0wn+xvPseSmYE+IhJrTjbRM+9Acbh246LZvDD5hZaP9ZL8CiGapXT9JqqOHGNQj5n4+oS1dXWarWT9elCrMcTGAbuatQ+1RsXkB7ow+YEuADhsTg6uyWPTl6c4/ONX/D7CmBK7EtB3CJmLPyfq2hmUHtpD5o//a4EzaYSXBsMaFRUVpKWlYbVa6yzv169fG9VICCG8hzUzm6Lvl+A/6WKCug9p6+o0W8neVCpTcom8dvg57WfopBCGTgoBqp+Ip+wvZ9UX2az7fhUFK5fWltMGBhF14yyy/vcJAcNGglpNyiv/Pqdjn89aMtZL8iuEcIuiKJTv3E3h9z/i07M7If6d27pKzVa6dSvFy1cQdNlktIGuNRFzhUanpu8l4fS9JJy35l1AVWY6TqsFrX8gpvjOnF74EcaoOEyJXTj60t9a7LgNUlTVL3fKdwC5ubnMnj2bJUuWNLheRnwWQohzYz2VQe57n6Ay6Am4dDwkt3WNmqcyNZfj//gG/wGJBF/Yq8X2q1KpSOprJqmvmVM9plKVnoqjrBS1wYipS1dK9+zEabMSMn4SmZ8tQKmqarFjN8gL470nYr2M9iyEcEvRT0vJW/ApPn16EXrz9W1dnWZzWm0ULFqMeegQAsaP99hxdP6B+HXvS0DfIfgmdEGlUqHW6XBUVqA4negDQzx2bABFcf/VETz00EMUFhayefNmfHx8+Pnnn/nwww/p2rUrixYtauvqCSFEh1Z55DiZz72GSqsh8uF7URv0bV2lZjv14Rp0Qb50+fvVqLUajxxDYzTi27U7/gOHYO7VB7XegEqnA6cTW3ER+ogojxz397wx3nsi1suTXyGESxwVFZSsXkfxz8sJnDKZwEk1CaNrg3K1N46SYpwVFZgHD271EStDLriY4n3Pk79xOYm3PUzhzo3krvzBMwfz0mbPK1eu5Pvvv2fo0KGo1WoSEhKYMGEC/v7+PPvss1x++eVtXUUhhOhwFIeDip17KfjyO4xdOxF+722odB07XahMziVodA80JkOrHtfcux8FK5aS8+2XxN71AIaYWHIWSjcnd3gi1suTXyHEWSl2O8XLVnLqiacpXrYK/0suImDixW1dLZfYFRsWZ8OjT6sN1UHQeUb/kdZgCIskfPxUCrauxWmpwq+HB/un1jSDcufVAZSXlxMeXj2tVnBwMLm5uQD07duXnTt3tmXVhBCiQ6rYe5CMf75A3gefoU+MJ/T2mztE4qs4nNhyihqdh1dt1OG0tH6sV+v0RN04C0vGKcoO7MXco49nD+iF8d4Tsb79f6OF8EKbLa6N+HfTuvvOul5RFMrW7aT8qw0oDhtdxt/O6V1L0OpNJI6a3uA2Vj/XLnbdfkwns+wwJwu3UGErJD5gIJ0Ch2NI9oVXs2vLLTn+gkv784Tcaxvvu1Oek8rRH95CcdgwvreJoGkXoU+IQm0yoNJosBWfBkAdoaCKqu6H43DjfuBrO8e5VK7zuoaT73D7APJVS8l4bx4VlbkuH9ddKqX65U75jqB79+4cOXKExMREBgwYwDvvvENiYiJvv/02UVGeb14mhBBNcTXWA9y88Y6zrq86kkrxT+up2H6cruNvpzwvncK0ffS87EHUWl298uVRrsV6Ve8KLEdSKfp+NZV7j+HTtwvhD12PITH61xK/JY17Xv2Ly+fTkgwr/Rtd57BaOPblS1iKctFFBhM0bQy+Q7qj9jGg1utwlFdiLarEqvKhqNyndjsdrvUVfXLzFS6V67yssf68IZQGdaHgu+/ItpW7tK/m8sZ474lYL8mvEB2U4nSS//63lK3dUbvswHfP1/67seS3KU6bhYIDWziatoIqeylhpiQGREzFz9CxRnQuz0lFcdiIHTmNnIzNZD796zzDajUasw+OskoMXeLw6d360zY4nXZy8w/hcFg9mvgCXtkMCqr7AWVmZgLw5JNPMmnSJD799FP0en2D8woLIURHVbZ+N7nzvq7tpHls+Xu16yqLs/ENiXV7n4ripOzYQXK/Xo7lWBq6mHDCH7wB07Derd4V6FzYygqxFOUS3Gs4Vv9ScuZ9X7tO7WdCsdhQ6bUETGj9kaoVRaGoOBmH3YLNw4lv9QHxunjviVgvya8QHVBt4rtuJ/5TxlLyw1oAghL7o1KpKc8/1ax95u1dR862ZTgsVUT79iApcGiHS3prBCb2JWP7z1TmZxDzzH1YjqXjKCrFUVKOo6QMtdmE3+gBqDQtP/iFoijY0rNwllditYaj1/tSVVVEUUkaVVWFZGTtoLIqn+DAzvj5xaLR6DmZsqzF61FdGe8b/RHgpptuqv33wIEDSUlJ4fDhw8THxxMa2rx5HIUQor2pSXzNFw6iYusBnBVV+ARFE5TQh4zdv6A1uD9HfXnyUbKWfYs1PxtD9wQiHrkFnwHdUKk7Xm9IQ1A4poh4StOPkvDeH7DlFGJNz8FZXoW9oAQ0aszDe6GP8szgkva8Iuw5+VRWBmI0BuFwWCgqSqGyqoC8vEMUFp3AZAojPm4MAGnp6zxSD8Ar470nYr0kv0J0QCW/bKJs7Q5C77kO86iBxGlHoNEZ0fsGkLzuMxzWSpLXf4E5PBGtwZfAuF6o1PWTPEVRcFSWUXhkBwUHNmMpzCW4zwWED76YxPUeHpLfwyryM1CcdrRGX1QqFcZu8R4/pmJ3ULpyK6XLN2PLqH6imwWo1TqcThsAGo2e4KCu9Ot9I2bfSADs9ioPJr943Z1gm81G9+7d+eGHH+jVq7rpu8lkYtCgQW1cMyGEaDmWlAxy31mIecxAQu+4Csd1l+C/TMEYEE7Byer+jqd3/IgpNL421jeUDCuKgmK3UXbsAEV7tlCechRTfGeiLpuO+tLI1j6tFmUtzsdeVYnW6AtqFfqoEI8lur9XufcIJUvWU3XgOADZgEqlQVGcgIIKNX7+MfTtfTOhoT1RqVTY7VUeTn7xqnjvqVgvya8QHYiiKBQvWk3RdyvxmzQS86iBAPgERtSWMfiHoc5Opjwvjfzj2wDQmfzR+fgTO/hyCA2g6OhOyk4fpzI7HcXpQKXW4N+5H3GX3IApMuHXPWW09um1CFtlKZnbl5B3eDMBCX2IGjSREhf79pwLS/Jp8t79BtvpHExDexN8yxQ0wf4Ef5CF1VaGQe9PUGAndDr379KfEy8LhgA6nQ6LxdKhmuYJIYQ7KnYdIe/979DHhhN6+5Wo1Gq0Qf74BBqB6riu9w2iqiSPgpS9oDhRaw0Y/IIJ7jQIk/8wSg7uojzlKBXpJ3Faqm9o+8QmEX3Fzfj3GohKpaKKhselaO+cDjv5+zaQtWUJWlMACZNuwdEKMcFRUkbBR4uo2LoPfec4Qu66DkPnOMI+yaeiMh+txkBgYBJGY1Drxygvi/eeivWS/ArRjpVv20/Jzxsw9uqESqfDWVZByZL1+F86iqDpkxrcJrr/BKL7TwDA6XRQVZhJzpFNlGWd4Ogv7wCgMZowx3YlcNQAND6++MV3R+tjbrXz8oSq4lwytv1ISfphVGoNcaOuIbTnBahUavBwcC9bt5O8979FHxNB1D/uxZAUU7su4tdRCtuMlwXDGg888ADPPfcc7733HlqthDIhRMeVesJK5j//iz4pGk2AH6hUFH27EkPnOELvmtbgiMv+UV3pd93fAFCcDuxV5WQfXEt5Xjqnd/wIO35EpdHgE9eJ4GEXofMPxCcmAUNIRL19dSROu5XMjT9QdGIv9opSQnpfQNTIKWj0Rioo8+ixrWmZ5Lz8EYrVRug9MzBd0L82MQsJCcXzz5ub4IXx3hOxXn4xCNGOWY6nYzmaiuVoKmqTEafFSsAVFxF03USXtlerNZhCYkkceR1Ou43CtH04zTr8Enuh1njXf//TWxZTnp1MxIDxhPUciVpn4PTWHyhK3ofyowrzBf0InHZRbR/fqqOplPyyBXteEY6ySpzlFdzna+WS60OZcGMo5gDXPp/SVdvIf/9bzBcOJmTWlR1iWghvsGXLFlasWMEvv/xC37598fX1rbN+4cKFbVQzIYRwT3aGg6rDp6k6nILKx4BiteHTuzPhD16P2mRscnuVWoPO5E/skCkoikJp5jFKtYWYu/ZFa/JtcvuOpODQNvL2riek7yhC+47CGBxJ4ZEd5OxYgf3LKny6xxM6azLagOrztuUWUfTDJiypWdWxvqwSp8XGC1O0zLg9gPjOepeOaz2VRdZT76CLDCHsD7egDQn04FmKGp6I9fIrTYh2LODyMZT8vAH/y0YTPONSFKez2QNSqLU6QjoNcnmqo45EURTKMo8T3nccUQOrn3qnb/qWvIMbCe0xgqooK4XfrsaSfBq/i4dSvGQjVQdOoosOw9A5Bn1SDBo/H/qqdvP165l893YWk2eFM+W28LMmwVWHk8lf8D1+44cRPOvK9tkM1wsHwAAIDAzkmmuuaetqCCHEORs2xgd9pxhwOol55v5zivUqlQr/6G5oXJzqqKMpO3Uc3+hOxF5Yff0vSTlI2rJP8U/qjU+/IEpW7SL98XcJv+NyyrYcomTNbtRGPaZ+ndHHhKHx80FxONmwfCM/fFHC+Klmbn0wiISzJMGO0nJyXvoQbXgwEY/fhdrH0Fqn6x4vjPeeiPWS/ArRgvKdrg1lf+++2xpd5yirIufrDdgLy+hZtZo8g4HD735x9vnMbna3pi54zQP7bGGV/g6cNgsqjRaHtQpVRCBVQdXr8o5sIWjgKMLGX0XIYTuFw/pxcNvHVOw8gq9/ND2H3EJIVO9fm0UDFtCljmRUlzJScjfz3VvbWDSvkISw4cSFDMagq9ssvKu6gvUHvybQJ5YhBRNQv2xpsI5Ld/yjyfMoKSkhIOBf5/RZNMab5v1btGgRkydPRqfT8cEHH7R1dYQQ56kUe6lL5V48Pa3RdYrDSfKiQ+TtyQAnWE+e5quvvuLaa689+05vcKem3qG4sxNneTkas5kqpQRtVCAlXaqDVfbaLegiIwh+cDb+KWqs11xE8vf/JeOZT9D6mIkcMZmQvqPQ6OsmrMo1UzEd2sqalctZvvgUQd0GEzZwHD4hdX9rRRQrJG/+Hkos9BpzP4Z1fg3WccWquU2eR3Ws/2czP4WmeUu893Ssl+RXiHakYMUeMt9fjmJ3YIwPo1Bt55NPPmn2RN7eLDk5mUMv/xmcTro98C8MoZEUbF+Lb0JXdH6B+MZ1omjfFgyhkegrwjEHxjH4orlYqorwD05q9CmtQWeme/QlJIaNIDlnI8k5GzmRvY4QcxJRQX2ICOiBVmOguPw0lZZC+iZchVrV8tMltRgv6gM0bdo0srKyCAsLQ6PRkJmZSXhb96kWQgg3FR3LY9v/Lac0pZDgPhFYCir561//Kq1ZGmCz2cj98GPKd+8hfNZMDHFxlG7eQtWJkxg7d8LYqRMVe/dR8P1itBED0Jr86DLjD5SfPok5tgtqXcNPdNUaLSF9RhLUcxgFBzeTvWM5hUe2YwyJIrDrIIK6DULvF4SiOMlP2UVUrwsx+Aa18tm7yUvivadjvSS/QrQDTpudrA9Xkf/jNvyGdiXm7knoQvzZM8UzTwO9wc6dO8HpBKoH8Iq+7EbSF77PyQUvkjTzYWKvuJWMpV+SufRLMgGtzofug24gOKKHS/s36Mz0iJlIp4jRZBcfJrNwP/vTF3E44xfiQgZjdVQA4Gfq2NNEdCRhYWFs3ryZqVOnoihK+2xmLoQQZ5Hy42EOvL0ZndnARe9eTXDv6gGonhr5VBvXrH0qLy+nfPceAPQxMfh074Yl/RSZb7xF+KyZ+F84BsXpoGjJz5TY1gAqIkZMInzYJb+17DoLtUZLaN/RBPcaQWnaYYqO7iR7+y9kbVlCYJcB+AfE4bRbMAXHevhMRQ1Px3pJfoVoY4pTIe2F7yjdfozw6aMJu3Ykahk0qUlTp04lfOxl+CZ2R6VS4RMZS6dZj3D8nacpPrCdsJETiZ16C9Yxl2E+XELakWUc2PIB0Z1GkdjjUjRa1wa50GtNxIUMIi5kEJXWYlLztpKWtw2H00pc2FA0ap2Hz/TcqHCzGZTHanLu7rnnHq68srpvtUqlIjKy8RsPDofnp7cSQgh3nPz2ALv/s5bYCV3oc+8FmCI69iwLrSEwMJCwW24ClQpdWCgAUfffS9Zbb1O8Zi2+/fsRePE4/IYPw7C/iOIT+8jevJTyUyeInTADvX+wS8dRa7QEJPUhIKkPDmsVhYe3kbN7NUXHdmIKisY/sqsnT7NFeEu893Ssl1/YQrSx/J+2U7LpMAl/vY6AEd3bujodhl6vJ3TEJXWWaU1mdEEh2IoLapc5bVayUrYQHNELg08wGSfXk5exlyEXP+pyAlzDRx9Aj+gJdIu6GIuzHIPOv0XOxaO8aACM//u//+P666/n+PHjXHHFFXzwwQcEBga2dbWEEKJJxcfz2fvaBjpd3YcBj4xp6+p0KObBg+q8V6nVGOLjKNux6/cLKTyyE43eSNSYqWSuW8ThD56iy/UPY4qIc+t4Gr2R0H5jCOk7GnVmEVqjuWPMkOEl8d7Tsb4D/CWF8F65328h871lhEweLIlvCyhPP4klJ6M2Ka44nUzaV++Cw0lW2jZqOrg4nXacThsa3Et+a6hVGoz6gJaqtmd5SR+gGj169KBHjx48+eSTXHfddZhMpraukhBCnFXBoRzW3f89fvGB9Jkzoq2r0+E5LRZKt2zD1Lc3AI6yMjLffBtbVjYqjQbFbqst67BUNvs4KpUKvW/guVa39XhRvPdkrJfkV4g2Yi+tJOujVYRMGUr0Xa7N2yvOrvT4frR+gfh374/DUknGT19gCI1i4IDbcThslOQno1KrCQzrhkbTvpsrtxgvCoa/9+STT7Z1FYQQwiX7Xt+IOT6QC9+6Cq3PeRJ7PKjqZDLO8nICLroIxemk4PvFOIqK6HbTH9H7B1OemYK9shRzbDd0vg2PzuyVvDDeeyLWS/IrRBuxF1egWO2YesTIwD0tRK3T46iqIGvZN5Qc24disxE7bTaaPANlxRkc3vkZKlSERvcjqdflWCqL0Bv9MPgEtnXVPcZbpj4AGDhwoMv/V3bu3Onh2gghhGsqskoJGxwjiW8LURurpy0qWr4CW0Ym1owMQq+fjjEkEoelklPL/4ettAhTdCLRY69CY/DBabPgExbTxjX3LG+J956O9ZL8CrdstthbfJ86ldOlcgP1rn1d1TRvYviW8EFRP5fKBb7jj6L4kRPShYJ3tpB0eGTDBae0YOW8kDmj7pXbGDsWe9ppKk4cJSS6D1F9xmOwBuHQK6SfXIvBFEx44lAyjq4m51T1BTMktj/dR9xSvYO0TJePvbTgvy12Hh7lRXeCr7rqqtp/V1VV8dZbb9GrVy8uuOACADZv3syBAweYM2dOG9VQCO9wwl7mctkKp2tTvZUrriV+Iwzt/6fpJ0VDXSqX93QiABGBo0n56WeiLdehNzTyJPLHFqqcF/JNr5sImTRJ2IdfQtHhnRhDo4mZcTW+MZ3wP+Eg4+Q2bGXFJPWZQk76Do5/8XLtdqOveuG3fR4vdOnYP+97umVOojV4Sbz3dKxv/1cYIbyUSqXC4BOAzVre1lXxGlq9D90uug2AzAMrObZmAVqDiaDQrhRmHsAcFIfdWo7dWlG7jY+fl88T6yXBEOo2f7rjjjt48MEH+de//lWvTHp6emtXTQghGmUwBrZ1FbyKSqUiYvRlRIy+jLK0Y2Su+haNjy9mAqkozQbFidNhw+n47YGNb0B0G9a4lXhJvPd0rG+7R2RCCIryThAY1qWtq+GVTu9bhq2iGGt5Ean7qm+plxWmk5e+p07CawqQeXo7oq+++oqZM2fWW37zzTfzzTfftEGNhBCiYcX5JzCZIxt/6iuarWDXeqpyMlCpNWSnbqW0IBWAU8dWozf+9nmbA90b8Vm0D56I9ZL8ilaRm2Un9biV/Bw7lqrqZs5fzi/mtX/lt3HN2pZWZ8TpsDVdULhNZ/QjOGEAnUfdBECngVcT1XUsif2n4heaVFtOrfbuBjA1fYDceXUEPj4+rF+/vt7y9evXYzQa26BGQoiyUifHDtvIyrBTUe5EURR2bbNw3615lJe51sXJG2l1RpxOifWeoPX1wxAcRuLVd6LVm4jqNJrYruNI7D2ZsNiBteXUatea53dk3hjvPRHrvftXn2gXvlpQzOtPFfC71id076PnyH4rAJOvMtGrn6GNatc2LJXF5GbsprIiH7+g+LaujtcpzU3BUpaPb2gcRv8wUKlJ3vM9itNB5rHqMjE9xnP68Aoctqq2raynecm8f2d66KGHuPfee9mxYwcjRlRPHbJ582bmz5/PE0880ca1E+L8s2eHhYfvyqcg77ckNzZew6k0BwCLvyhl+m3+qNUd4xrTEhx2K/nZB8jPOYyinL/Jv6fYK8spPXkI37jOAJj8IshK2YTidNSW8Q/phKI4sNu9PNaDV8Z7T8R6SX6Fx1RVOvnivWL++2IR1832Z9xlvpSVOMk8ZWf/jiriO+s4sMvCrKlZTJhq4tGnggkI9O47c/npFXz/9EGOrFuKSq0hJLwn8V3Ht3W1vEbhqQOkbvsWa3khpuBYguP7o9Zo6TzoGvJP7SGmx3gMPoHYLGWYg+PJOr4BS2VxW1fbs7ykD9CZHnvsMTp16sSrr77KZ599BkDPnj1ZsGAB06dPb+PaCXH+UBSF1cuqeOKRArp01/GfeQFUVigUFjjYutHCoOFw4qiN158q4Jfvy3js2VC69fHuG97WCjvLXzvMthVLcTqs+AclkNB1QltXy2tYi/JJ//FjKrNPodYbCBtW/Tsqqc8UUg8tJTiiJ+FxgygrPo1/cBJHdnyO1dtjPXhlvPdErJfkV3hEbradP9yYRdpJG7feH8CdjwTVGbb82lv9AXA4FH75ppSX/1XAM38u4P9eDsHH5J2t8Utyq5h3yxZ0BjXd+l1LaFQftDqftq6W17BVlXFi/SeYwxKJHXAZwfH9UGuqL3ERScOJSBpeW9ZoDgHAHBRLQcYBoruN9drmz94y9UFDpk+fLomuEG3I4VB47IECli6uZOSFBp57IwQ//99i+JSrfWv/vWGLgxf+msff78/hpY8iiYn3zml/nA6F/83dTuqOfOI6XUx4zGB8TMFtXS2vcmrJZ9gry4i6eBr+Xfui863+TekXFE+fkXfWlgsM61q9PDCWtCPLsFQUYTAFtkWVW4W3xvuWjvXemWWINmWpcvKXu3KoKHfyybIY7vpjcKPzdWk0Kq6YYeaBx4NY9XMFP37tfSMfO2xOti08xX9v24ZKBXM+GUFk/FBJfFuYtbwIp91KdO/xhCYNqk18zyamx8WUF55iz7KXqCzNbYVatgGlGa8OxGq1curUKdLS0uq8hBCe9+K/S1n2YyXPvRnMmx+G1kl8zzRgmJF/vRnOqRQ7bzxV0Iq1bD3JW/P47IGtnNiUy/WvDiWh6wRJfD3AUpBDQPeBhAwYVZv4nk14/GB0BjM7V71E3um9rVDDNuLF8b4lY713PuoQtZy41sdkk4tdIW755d6zrlcUhfwPv6Ri/2m6X/4AL33W9Oh6o+7fxoYju9EYiskdNIpP8n0bLPeW3bWO7Uk+rg2idW/wDpfKPZ7helOl3fN+m+fX6bCTe2gD+ce2UVmYhX9MN2IuuJwlH8WwffFcl/cpGlfU7bebKoojGs06PzKLduEc27VOuduvWdboPk4fGs7nf9pD+uHXMQ75EzqdyWP1bRPuDmrRQYLhsWPHuO2229i4cWOd5YqioFKpcDgcjWwphPdp6Vg/99CsJsvkr9pP8tuLiRt2JV9tH8tX289e/sI/bCH1ZDZwGvNFfVlYlNBguc8drv00jTS43ox1ZqBr8f6JjEtdKrfty3513pcc20fhvi2UnTyIISSSmMtuYeexgez/8WGX6ygaV9q57vXc0KcrRcl7MN0yAZXmt+5yV926odF9jMofwZKndnNs7aeMeDCVVSVTPVbfNuOF8d4TsV6SX9EiFKeTqsPHKdu0nYqtu0m68CZ8Q10bVv7AdyfY8eFhht/TB7+ohhPfjkZRnKRv+Y68I1sIiO1Owujp+IbJwFaepNJo8Ovdn7LD+93aLqanP7PeHMQb128i9egyuvS+0kM1bCNe2AcIYNasWWi1Wn744QeioqIabV0ihGhZlWl55K86QPa3Wwm5pC/hsWNc2i73aBGL/7SRmEGhdJ/kPdPOFB/Zw6nFH2KMiCNm8g0E9Boi1yMP8x0ygIrtu7Hn5qOLDG96A8A3xMBVzw3hk9s3sPiJnfg+MsX7/k5eGO89Eesl+RXnxFlRSdnG7ZSu3YQ9Ow9teAhB068g2DyoyW0VRSH30AZ2LthO3+u6MOzO3q1QY8+xllmxVZRQfOowuYc2UJF/iviR1xLW44K2rtp5QVEUKtOSMUTHur1taIIvQ6+JZdOne+jUc4p3TYnghcEQYPfu3ezYsYMePXq0dVWE8HqKw0nRpqPk/LiT0j2paP19CLmoN/FzJqL6vukfo2XZyXx9zxoCYnyZ9upo1JqO2+vObrFjKy/Bmp9N/o61lJ44gH+3/sROnel9yVQ7ZTmRgsrHiDYsxK3ttHoNY+7twef3bESXdgpDgvfchAG8Mt57ItZL8ivcptjtVB06RvmOfVRs34PicGAa3JeQW67D0CWx+uK/pYl9OB1kH1jL6W0/MODGboyZO/Ccg0ZVURUrHlnBkMvDGXFTp3PalzvyDuez7l8bKTj6Wx+mgLiedBt2L35RXVqtHucza0E+BWt+wZJ5mrBJVzRrH4OuiGHtBynkZOwmMnZwC9ew7XjrABi9evUiLy+vrashhNdSFIWK41kUrj9C/sr92PJL8e0ZQ9KfriBodHfUuqZ/QiqKQln2SY7+8i7R/QK54sWRGPz051Yvp8KqZ7aiKi/nhpda71ptKbWw/h/rOb3pNE5bdTNzQ1gUUROuI6jfcEl8W4HTaqVkxVpKV2/Ab9zoOk2eXZU0PAy/CB9KVq4jbPaNHqhl2/HGeO+JWC/Jr3BL1dET5L3/BY6iYrQRofhPHoff6OFoAvxc2l5xOji9Ywn5x7ZhryojrNdoxv7R/Sd1O9/eSXFyMTEXxGArt1FZWMmxRcewFFn45XA+iUNCieze9CAIzaUoCvmHCzi6+BjHfzyBf5w/Y58cRcbGHuh9g/AJjvLYsUV9+at+pnRPdZ8uW2E+0PXsGzQgqpsfweE9OZ28zquSX2/13HPP8eijj/LMM8/Qt29fdLq6I8f6+3vu/78Q3s6aV8LJ5xdRtj8drb8PgaO6E37ZIEydI1zeR/6JHWTuWUFVcTam4BiueWs4Oh/3fnYmrz3Nro8P0eWSOBQnWMttHF+eRv6xIgAOrcyix7gIjyae5dnlnPjpBEe/O4q13MqQB4aQldwXtd6AKbYTKlXHfYrd0VTs2kfx4qUA2AsKUZxOVGr3Pn+1RsXI27qy9N+7sV89BW2AxIr2zBOxXpJf4bIf/ldC9kvvYuiaRPgDt6GLiXQ74BSlHSB73yrCe48ltOuwX5PEbWfdxl5lpyi9jONbCzj67VEqCyqxFFkASF2VitakRe+rJ+GiBEJ6hHDo4928PX0N/afGUVFoYfRtXUgY7F7TmKbs/XA/29/ciSnMh57X9aD/rL7ozXrK03u16HGEa8ImTkUXEIQlK4OcH77B1KUHuoBAt/cTETOQQ7s+w2opQ28wt3xFRYu55JJLABg/vu482TLglRDn5sQhCwfvn49Kr6XLk9cSMKQzKjebKduryklZ9wW+YfF0nXgn/lHd0PmcPdYrToXCtFKyjpWz5/MjFKeXUp5bCUDGzhw0Bg16Xx2h3QIZNPMCjn1/hE8f3EGPcRFYK+30vyyGAVfEoNG2XDKasy+Hn+/+GZVGReL4RPrO7Etgp0DKv+zY3bQ6Kt/B/XGWlWPLzaNs7SYq+vbCd+hAt/fT5/JYlj67l6ojxzAPk5vd7ZknYr0kv8IlKcesvPT3XMyjhhF809Vu32kDqCjIIG3TQgCCkwY0+XQ053Ahh39M4cSKdEqzKgBIuDiBpAlJBHUNIm5MHIpTqdd3aML0YD6YvZHd36cDcHRtDvEDg+h7eSydhoUSmnTuSU3y8hQSLorn4mcvRN2CgVY0j9bPn9BLLsNWXETyi//EkpXRrOQXam7mdIC2QK7ywj5AAKtWrWrrKgjhdawWhScfyEYXGkS3p29AF+D+6Pe2qjIydv6Mojjxi+5GQMzZ++qV51Wy79tkTq7LJHNv9WwNYT2C6DWtM+YwE90mJ6LRq+vF+gunBbPs1cOs+e8JAE5uzmfFm0cZfkMCCQODiR8QhFpzbk+E09akoffXc/XXV6M3n1tzbXHuVFot/uPHAmA5egJLSlqzkt/aBzcdJN65zAvjvSdivSS/Xu6lAteaf/7vlYlnXZ+y6jPUxgrylq/FYDA0ub/xXzxb573NVsGO9S8CkJgwjvDiILTl1U9vN74+tLacoiiUZh4ja98qSjKOojMF4BfZne6TLyDioA1TWiCkASsg++2Gj73+9n44Bsfip9pO+ZFDOCsryMo0kvbMAXA68Rs8lIhrb+CCGceaPA+Acnv9gKcP8cXuUFGJEey/LQ9Itri0T9EytP3qTnXhzK5+r+1SVWfdpkLX+oAbC6v7lRgySjHqmijcQXhjHyCACy+8sK2rIES78U1ZkEvlnvjs7H0cS/ZsJ+tkMvv2rqBPnz4u7XPoly/VeX98+XzKc1MJ6zGK4MQBtT+wl/93RJ1ylqJccnatovDIdlRqDabIRBImXU50igmzIRTV1upkN/nHho/7y7RA7P5DMPf+HlAoO7AHiyqE5W+eRLEdQRcUTOL9j6HSahlyy0mXzsWssdZ5HxSmx1ZuIzgA4Ld1oZeedml/omVMHb6r3rKP9ZUkxeQx+nfrdhe51o3OYa1+WhixwUL0sSbm/5rjej3bmjfGe0/Eekl+RZOsZYWUnDpEcNehLiW+DdFofksgS8sy2LnzHcrKs+jadQoBieMAKM9LJ3XTN1TkpeMTHE2nC28mKLEfql9H3jUdy3L5eKau3TF17U7ZvuoBufwGDMJptZL9xcdUHD6Ecg5NIo8vPEjmxnTixrfeoFrCNZaU6u+ILjywWdtXWotRoUKv9b65fr1VRUUFaWlpWK11f7T269evkS2EEA1RHA6Kd21DHx7pcuLbEJW6+qeltbyQ1I1fUZaTTFBCP+IuvxWVSoW9soxTaxdSfHwPWh8zEUMnEdpnJBqDDwB+2UUuH0vrH0D09JlYsk5jjIol8IILUanVFG1ZR+7P32PJycQY3bwRfbMP5LPuxZ2oddK6q72pKrFSmlmOf3TzpseszCoBwKD3wv6+XhrvWzLWS/J7HstLLSdlZ2F1MLJUov018ChOBzXNPy2l+Zz4+V3UWj3hfVyby68hKpWa+LgxlFfkguKkrLw6Sam58JRmneDYsvcwBoTTdeJd+Ed3a5EBLMx9+9f+W63XYx4wmPJDB7Dm5TZrf4pTYfcbW1BpVHSf0fwfB6Ll2UsqyHznJ0y94tFHBzdrH7klxwgyJ6BWyVRH7V1ubi6zZ89myZIlDa6XPr9CVCspsHF4WxmFuTasRXnog0OB6pZWisOBSq1GsVnJ+vZzKtNOEnPDbed0vNCuQ9HqjSgolOUkA6Az+aNSqbBVlHLiuzdxVFUQe9G1BHUfglp77s1sDJExGCJjat/7DxhG7s/fU5lystnJ7475BwAY++iQc66faDmKorD8iY1o9Ro6j49v1j6yNySjVmkJ8JOpjto7T8R6SX7PU2vmJ7PkpSMoCqhUoPM9RdSQyZRlnaTwxG6cdiso1UP56/2C6TrlPvRm15pVNUSlUtOly2QAysqzyd96lPj4sYSH9yH99BFOrv4I37B4ulxyOxqtZ/rVKE4nRWtXoo+KQR/m2qToZ1KpVRiCfCjPKGXHSxsJ6xdB9xv64RspgyO1tZJ1+7GXlJP0nzubdePEUlRJftlJukWOb7pwB+KNzaAAHnroIQoLC9m8eTPjxo3j22+/JTs7m6eeeooXX3yxrasnRLtwaGspL9xznIpSB2oNKKrnCZ0wBUdFOaV7d2ArLAC1GpxOVFot0TNm4du15zkdM7TLUEK7DMXpsLPz4z/jExxN/PBplJcUkLzkAxyWSrpc/QCGwLAWOsv6CtavQKXVYu7Z/JvUPiFGoDoJztiZQ59ruhA9sHm/HUTLKUorJWVdBhOfHolvqI/b2ysOJ+k/HiLQPwGtpnmtGdsrb4z3noj1kvyeh7KOlfLzK0cZPTORS+Z0oaLYymvXHyN19Wfo/YIJ7z0anW8AKpUatc5AQEJvNLpzu0BYLCUUFiWTk72H/ILqvrZpaWspKkqmpCQd/+hudB53q8cSX4CSLZuwnD5FzD0PNmvArhqXfnwNmRvTOL0+jdRl/9/efYdHVaUPHP/eaZmZJJNJ7yEESOgQeu8gWMCOqNiwr65ldVd3VdR1rati+WHHDmJDUFQMvffeEiCQ3vtMJlPv74/RsJEAEzLJQHI+z5PH5M65974Dknfec0/JJHtFJuPevhRDotF7wQpNVrM1HX23DqhDPNt263/lph0h/dPtKCQVwf6iJ/hCsHLlShYvXszAgQNRKBR06NCBiRMnYjAYeOGFF7jkkkt8HaIg+JTd6uK9f54grouWv87pRKBRyX0zlZT88gMKrY6Abr0IGZnofvqrUqFP6oLaeG6jZv7gtFsxlZyg4sReKk7sAcBSns/RFfOozD2EOiCIpKl3tWjhay0upGLjakJHT0IdfO47PYz+xwBSpiRyZFkWeTuLWXTnCiY+N5TkixK9F6zQZMfX5KH0U5I4Kvbsjf+kMr2Y9A+3YMquoEuHwS0QnY+1wXzfErleFL/tjCzLLP7PQULidUx+MBmVRoE2QEXK5Q9hr61GExDs9f3yior2cuDgVwD4+RkwGOKoqsoCQJKUdBp7M8YOPVt0rzyn2UzZbz9jGDAYXYfEZl1LrVeTMKETCRM6UVduYdX9S1n5l5/o9/Aw/OREsdG9DzgqTJj3HSf6rqb/ErSUmNj+bBqGpBAGd74Vg87zfSwvBG2xJxjAbDYTEeF+ChMSEkJJSQnJycn06tWLnTt3+jg6QfC9n+YVUZJn45F3OhMW7e5YjrpiBqFjLkIdZERSend6h81cwf5FL+Ny2FCoNAREJFKdnwGA1VxB9NBLCO0xFKVG69X7/lnJL4tQG0MIHj62WddRKBXEpEYQkxqBy+lixTNbSHtiE1U5JtQXh6HUtZFVES8gsixz5NcTJI6IQaNv2p+/7JLZ88IK6kpNpD45kdDlw1soSt9pi/m+JXK9mMXfzpzYWUHmtgou/XtXVJqTf/0KpQq/wJAWKdycrpOT063WaqqqslAoVPTtO4v+/e50L2rVwpvE28vLcNVZsJeXUrZsKbXHPFvp+Wy0ITrGvnUJwSlhbHxiBbt2f4jJ5PnCXELzyS6ZwnnLUGg1GIY1fZ/l3a+sQROkY8Rbl7e5whc42RPclK8LQEpKCunp6QD07duX9957j7y8PN59912io8+8jZogtHW2Ohc/fVjIxOvDietycmioJEloQkK9Xvj+fnX3lCnA5bDVF74xqZPpMfVvRKSObfHCF6AuPxeX3UbZ6mVU7drarAUu/6BQKhg/ezB9b+zKtg/2s/2OTylZm+GFaIWmOLQkk5L0Crpf3qnJ555YtJea42X0f2YKsRNTWiC680Ar5Pu5c+fSsWNHtFot/fv3Z926dR6dt2HDBlQqFX379m3S/Voi14snv+3Mvt+KCIrSkjKy5YYc/Vl4WA+ydGsI8I8kOnoAWl0w/vrwFi94/5cmMhJ1eASWzGNYMo9RsXoF+yYk0GtQ81f11YboGP3aZPI35bD9iR3s2Pku/frdRWCA+ADeGoo/X071+v3EPnwVysCmzf9x1Nop2pxNn4dHojFosbRQjD7VBodBgXseUEFBAQCzZ8/moosu4osvvkCj0fDpp5/6ODpB8K39m6oxVzsZf13r5XqNv5HgxD5Yq0uJ7DEabVA42qDIZk+bagrZ5SIodRAVm9ZQsX4lAPaKMril+ddWKBUMfyCV7pd34peXDnLw2R/p8tfxxEzt2/yLC2eVtTGfVc9tpfu0TiQMbfrnq4JVR4kakURYf8+2Q7ogtXC+X7hwIQ8++CBz585l+PDhvPfee0yZMoWDBw+SkHD6xceqqqq46aabGD9+PEVFRU26Z0vkelH8tnHR6ooGP8dGudhaYcW/uhhj+Mn5tdYg7953xcrH/3TkWe/e4AxGXPnfBj9XlWaSvvVz7LZaQmP7YAh1D02u7lLH1tqz934/FtP4CnOnuAquqnyYnPfeYF/xT8RNvU8MgT5HI6/479kbAd0uWcfBRRsZ8WBf+t2gBtIbbbd4S/9Gj9ty88ElUyT1onJfIif2PHKuIZ+32uIwKIAbbrih/vvU1FROnDjB4cOHSUhIICwszIeRCYLvBUe4h4TmHbUQ/z9PfmmBlLTt44f/56eHT9vO20ZNfaXBz7a6ag5u/4yaimwCjQmExfQCWSZc3Zd9dZ698fvCV529UThsmPEwduV3HH1vPdaIESgDT7PWRNtaP9Hrhl/j2YJFne91sXz2ZmIHRzP08aHYZEWjhVuF9fQd4DU5VUT1Tapvs37jE+cU8/mspfP9a6+9xqxZs7j99tsBmDNnDsuWLeOdd97hhRdeOO15d911F9dffz1KpZIffvihSfdsiVwvit92Zuz0CJa8k8fCl3O48+WkNl+cWS1VHNr8Mf5BMfTuNx2t/8nFPNR+q71+P4XGj7DJ08j/9D3Mhw80a6VJ4ey2fnSAiG7BpN7o+RAm2e7AcuAw1uPZ2AuKQKFA1ZaLpVZ68jt37lxeeeUVCgoK6NGjB3PmzGHkyLNvj7ZhwwZGjx5Nz5492b179xnbPvyw5x+sX3vtNY/bCkJb07GHPz2HBfL1nHx6DjMQENS2P+7JsotDO77EWltBr6F3YQxr+rDYppAkieBLL8a8aw8Vy9IIu/rKFr1fe3f4hwysNTZGPTEMhdKzUYOyLFNzKJ/KHSdwVFmwV9Sii2/egm7nvXPM99XV1Q0O+/n54efXcMSGzWZjx44dPPbYYw2OT5o0iY0bN572Fh9//DHHjh3jiy++4LnnnvMorJbO9W37t6FwCv8gFTc9lcj7/8ikqszONQ/H07HnuW0SfiEoOLYO2eWi6+CbUWuaP8TZE/5dUtB1SqZ02U/4J3droblVbV912QkObf2UDt0mE5V46qqMToeN3G3FjH60H5LCs04c2eWi6O0PsWYcQxlsRKHXEXzNVJQGsVVVc7TWUKhdu3Z5FE9b79QTBE/c8kQCz9yQzhNXH2L6Q7EMnnzu2xWe7ypLj1Fdfpweg25t8cL3D0p/PUETx1Hx0y8EjRqJOqL1hpi3Jba6Gg5t+AhdQDjJg29otE3O+jzih8fiH+H557jc+ZvJ/mQ9qkAtaqOeyIt7Ezq8i7fCblPi4xvucjF79myefvrpBsdKS0txOp1ERjZcGyUyMpLCwsbXujly5AiPPfYY69atQ6XyvORs6Vwvit92aMQV4egNKha+nM3sK/cTl6yjWrLiFxaFNjQKSanEWl6MSh9AUNd+F2TxJssymXsWUXh8E5KkxOW0Y6kpQRsQ2ipzjcMuupScua9RuXkdwcPHtPj92pryosMc3PTR7z813o1ZVnAA2SmTMCTK4+tWL1+L9UgmEfffjq57G13w4s9a4clvaw2FWrXKgyGJgiAAENdFx9PzU/j8xRzeeCCTkCg1VoMFTUQkfhFRKAODsBUXggSBvfqh8r8wOwFLC/ZzaPtnAPjpg6mtKUarD0ahbPnVmA2jRlC9bgNl3y8m8s7bmrWNYntkra1k3+q3sdVWopAa/6xps1RTuKeYYX8b6PF1a9ILyf5sA7HTB9Hh1pFIHj4tvuCdY77PycnBYDDUH/7zU9//9eeCU5blRotQp9PJ9ddfzzPPPENycnITgmr5XC+K33aq3/hg+ow2svXXMtK317B5bTXm7CM4LWYAFGo/XHYbpdtWkXjtvaj0F1ZSdNjMFJ3YAoAsO9n+q3uoRXh8KskDrgegrsZO6QkzsT2DvP6kSBsTh3HYaEqX/YRfTBz6jp29ev227sjOhfXfH939HREJA1Eo3InR5XJQnL2dzH0/0mFYNMYEz/f1rVm9noDhg9pP4cu5zwHyZBgUtO5QKEEQmia2s47HPkwmfUcN29IqWbVWpmb/bioq3euBSCoVyDJlK5cRc/1t6BNb56mpt8iyTEHWlvqfd64+OQRy+MX/QaFUI7ucZO+tIqqzPxq9dz/2KtRqwq65kqIPPqZy2XKCp0zy6vXbutxDadhqKwGoKc+ipiyLwNAOgPvvtro0k2M7vsHP4EfSxESPr1v40260UUEk3DKi/RS+nHu+NxgMDYrfxoSFhaFUKk95yltcXHzK02CAmpoatm/fzq5du7jvvvsAcLlcyLKMSqXit99+Y9y4cZ4H60Wi+G3HlCqJoZeGMfTSMIren44syzhrTbgcdtSGYOpK8sn6ei5Fa38idvJ1Tb6+y+VC4aNe0Nz0lag0evSGaLT6YIqytgKgC3DvFeawW3hn+nrKsmtJGhzKgKsS6DYukuJjJsqyzCSPDEcb2Lxe47CLLsVamE/hV58Rf/eDqIPb+FwTL7JbTfXfqzX+SJICh72OwhObyT+2HltdFRHx/bn4lY5Nuq6zqgZ1jOdPituEc+wJ9mQYFLTuUChBEM5NSv9AUvoHktHB3fnrstbhqKlGHRyKs85C/oKPKVr0FYn3/8NdEDeBL3N9RUkGlSXpaPWhJHa9iMM75wNgCOkISMiyTMbub1i/dCfBsVrGzupItzHhyC6Z4zsq6dA3iND45k2J0vfojnHKRVT+/CuamGj8+/TywjtrH+rM5Q1+1ugMyLKLsrx95KevxlSRg39wHJd8NAKt0fNtsuzlZnQJoShUF97IxWZpwZFeGo2G/v37k5aWxhVXXFF/PC0tjWnTpp3S3mAwsG/fvgbH5s6dy8qVK/n222/p2LFpn9+8SXzqEOpJkoTK/+RTNF1ELMF9hlG+ewMxF13r0XDh48ePs2DBAhYsWMDRo0eZOnUqM2fOZMqUKShbcfh0RIeB1NWWk9R7GlZLFcU5O9y9v4eWUVdbjrmqAKfLxqSHurLtmyy+/vup8wu6jYvk6hf6gucPFhuQlEqip88k5903yPvkXeJuvw9V4Jl71gT3PNA/KFVaUgZcj8Ney541b2OtrSA8vh+xnUfhb4hCrdvRpGtrYqOxncj2dsjntXPtCW7KMChonaFQgiB4h8JPi8bPXUyo/AMIGz+F3I/nUleQhy6+w1nPr6ysZNGiRcyfP59Vq1YxbNgwZs6cyfTp08/6BMmbDMEJhMf0ITJhIP6B0QQExWKqyqO6/Dh7N76DLiCC4rydjL4tkfxD1Xz/7CF49lCDa4TE65j5eh/iup973MZJ47Hn51Py2Zco7pqFLlnMLfWE02Gt/z6+x2Q0OiNHtn5Jac5ugsI70234LIxRXQmK33eGq5xK3zGM4t/2I7tkj9cEaQtaerXnhx9+mJkzZzJgwACGDh3K+++/T3Z2NnfffTcAjz/+OHl5eXz22WcoFAp69my46GtERARarfaU462t/YwFEM6J7HDgstZhzjpyxnZbt25FkiSSkpL4z3/+Q69evfjnP//JwYMHueyyyxg7diwZGRm4XK5Wids/KJpuQ27BTx+MITSRYdNeZPClzxIS1Z3irG24HFZufX8wo2Z14uFfxnLpP3ug0igYNevkkK9DK4twWJ3NikPpH0DsrXfjstnI++RdXDbr2U9q5wwGA537XkVij4sZMOlxjBHJ5B9dh62umn7jHyG537X4G87t6a2ud3dq9x5Edjbv7/WCco6b3v8xDOqPr9MVv+c6FOq+++5DpVKhUql49tln2bNnDyqVipUrV3rlbQuC4DmFxv3v23RgzxnbmUwmOnbsSHBwMLNmzcLhcPDcc8+h0+m4++676datG2lpadjt9tYIG5VaR9f+NxAcnoxGG0jqqAcYfsnzdOx+CTWVOZTm7yW577Vc+kgyd340gIe+H4rKT8HQ607u9VqeY6GmpHm5WZIkwmdej7ZzEkUffIw1N6+5b61dSOw9lZjk0fSd9Cjx3SZQU3qc0pzddB44gx6j7yY4uts5TUsLGdoZe0UtNQfb2d/DOeZ7T02fPp05c+bw7LPP0rdvX9auXcvPP/9Mhw7uDrOCggKys8//BwziyW8bd2VAgUftfrv6wCnHZJfMtx9sJqxHOIPvqUXjf4DOL79+ajuHg8IvPgEgcOBgwqZeTtnIQtZTRsyo8Wh3pbD1+WWkpKSg1KmJv7w3CVf1RRvmnkdcPCMLi72SDsb+p13wAOCXjJc8ei8BazNO+1qqPIgaQxJBqghWHu4Jh39/QQNJ/3BSKMsodc/Uz32e/3kv7nxqv0f3Beg1tvFOgs79prDq9u9w7vmcPn8fzZTkf3h0vV8yXmL9+vW8+uqrPP/883Tr1s3jWFrL5JA7PGqXf0t3AGzV5Ziyj6DSBWDo1KPRtuM/1AE64AiVWdVsvmE1PWckM+DBPOBkMrsy2LMnvzt/7QdAQHkHDtRZCf+hAp1/6KkN7/bocheWFl7wqi0NhRKEC9Xl/mUetds1bUOjx5f8Ywt+gWqueFhFeJcNpPz71DayLFOzdweFJ06g8NPS4f6/k28wcmDsASLGxjP1nqvZ9O91TJo0CUkhET+6Az1v6UNIinsrucypZoosmcT790CrPP06Ir/kvunRe/ErPX3BmmQYTGjPGPTaUDRqf+YuOzm0Oe4/V1CoVBKQ+RWmrdsAWLQ0hlV143j4uuMe3XvyiMZXo7UPTuWHWSVYvv2Iiz+fwtDrPdvHFuDTpy/j1VdfZeLEiVx99dUen9eaev/4lEftaqp0uGrrsOw/grPaTOC4QY0uBjb1tVIgGsjBUXecb25bQViykSnvuJAU2+vbXR28zaP75hjded0ZJ3OrSmJA9SYmJEZ4dG6b0AoLXN57773ce++9jb72ySefnPHcp59+utHpU61NFL/CaZmLTFirrAybnYrGX3PK67LLhWn3TsrTfsVRWUnYtCswDB1xSi9dSGocwz6+kYo9eVTuzyd70R5OLNiBpFIgqRS46hwA5FXvI0TXAZAx2croEjqSYF2sV9+TSlITrI5u9LU/VrVOeugJbOWlZL37KtV7tzfatqkMHUPodf8wdr+ylqihCRjp7fG5d9xxB4cPH+aaa645L4tfT9hdVkp2rKb66D5q83//cKFQ0OuB/57xvMqsapbet4KA6AD63978eVRavXvetbW2vPHitw2Sfv9qSvumaitDoQShvSo6VEnPqR0I7xLU6OuWnBOUpi3FcuIYgb1Sibx8Ogp1w88FAdGBjH9rMsW7C6k8Ws6hBfv5+ebFSEoJpZ8KR637aXCOeR/h2o6oJDU19jKi9cnE+zfeEXquJEnCGNj4Nmt/5Pqw6dcSNGYMpQu/xrR9B0FjxzT7vmqdivHPDefbG39h81u7CFB4vkLx3Llzef/997Faredt8Xs2skumcs0+ytcfo3b3YbC7P99pEqLRJp9+OL2t1s6vj22k4kQ1V304vtlDlZUqiZAoNaV57Wu0XWvk+7ZAFL/CaclOd5eQucjc6OtVG9ZR9tNi/Hv0JPrm29FEnX4oqjrAj4jhSUQMT6LjDQMp2Xgch9mK02JHuzgInTqInKo9lNWewGx3L4BQVpvl9eLXEwqNH9qoWIwDh1OxdQO7dhlJTT21+G+qjtO6U7gxi50vrmZ4cCf8VJ7tr/ztt9+yb98+rrnmmmbH4AuyLLPHvJLyDUXoozug1AXgtJgI7TP8jOdlrshm9bOb8A/Tcclb49AENP/vwOm0AaBUnXn+apvSCj3B06dPp6ysjGeffZaCggJ69ux5QQ6FEoT2yuWUqSmyNDpH0lpcSM4Hb6IOCSPm+ln4p3Q/7VBUhVJBVP8YovrHkHxVd/K35FJbaMZhdWD+RE2YtgO55oOU23KxOS1YXWZcOL1e/HpCUijQREcRNG4sxZ98StXKVXB9868bkhTE0L+msv6V7XQdfRhjTFePznvqqafo3bt3oyNmLhSlizdT+PFyNB1jUUeFYc8pRNMxFk1C4w8dAMoyq/j50Q2YCmu59LWRRHb3zuKgdWYXWn+x4NVZ27dDovgVTisgJpDY4fFsfWkDBZtziR/TgdpcFa46C47qairSlqGJjibqptuadF11gB8xk04mA8UqdyESonOvLru/6Fdyq/eSFDLEe2/mHIQMH0vltg3s3mn3SvErSRL9Hh/Db9MXkFW5k+SwkR6d16NHD3r0aP0PBt5SZD9OmSOPwI7dqS3IwmWvI3r05YSmnvr+HRYTlYd2snTzFnI3FZA0IYHRTwzxSuELYKtzb9+j0bafhcdaegGMP7SFoVCC0F71m9GJla/s5dPrV9Lzsg6YC0KRnQ4cNdVU7dgMQPzt96MK8HwFSIVKQdzwk09fS79zDz3uZnT/7i+pO8GOsh/pFOj509GW4N+7F6qwUOoyPRvy7Ime05PJXJlDzr5lHhe/ISEh3HZb0z5PnU/s5TUUfbEKXXIsDpMNe34J/sP6Enrb5Sj8GuZw2eHAvO0AaV9vIeO3HIJi/Zn++URCkhofedBUDpuLmgoHxvCW3+v5fNJa+f5CJ4pf4bQkhcSYVyZydEk6R35IZ+Mza4G17tdUKvziE4i41gvdpP+jxJxJbvVeQnUdkHw0IMNlt1F7LAOFnx+SWkNujvcWR9IG64mf2Jm8n/bRJfTUIeJtkc1Vh4QCc14mLlsdybc8hl/wyTk4DouJmhPpmE4courIXgCi+oQw7rnhdL4o0at/RnXmcpAUqP0urH2rm0X0BAuCcBYDb+xCZFcjWz/NYNVre3E5fl/4SqFAbQwmduadTSp8z8bqrGVP+TIAAlS+2QZQlmWsmcdx1dWhMhpxVFbgdAagVDY/50iSRK/rUlj26FpqKwvQG0//5LOtcFlsyHYn9qJKHFVmwu6dTsDQPidft9mxHj5O7d4Marfsw1lZg9w5iIG3dSP1xq6odd4rSUp+H+4cEtW+il+R7z0jil/hjCSFRJfLu9Ll8q7YzDY2/5aAQqtDodM1qyiRZZnyHTmc+GoHVccKkVCSYOyLTm0EIESf0OqFoctmo3TVr1Tt3IzLWld/fN5HNo4fd/LRJ0avxBQxII7jPxzE7qpDo9Q1+3rnO63CHxkXkUMmUbh2CfbqSvyCI3DU1pD98+eYc44BMtqIWCKGTCKk1xBG3X76RcvOVW1NEXnH1hIUmoRC0c5+9bXTBCcIgucSBoSTMCAch9XJ91/1QlKpUOr9G12oqCmqsio5+MU+cgpycMoOgjXRdDOOxiHbCFSHoVN5r6j2hCzLmLZto+LX33BWVDR4LWWYipXfxZEQ1/yiKW6weypYbWVhuyh+1eHup7bBF6VS+tN2bFkFMLQPsstF+Rc/YVqzA9lmRxkahL5/dwInDuHqSUVej6O2xsH8F3Pw0ynoktq6/2+dF0S+P6t29glQaA6NvwZ1SPMWCbJVWShckU7e0gPUHCvFkBxBUvBQTLYSjpVvAiT06mAiA1p//8+CRfMxHzmIcdBIgvr0x1ZaQvFvS3BUVVBW5vJaMa6Pdg+5NdvK0OjiztL6wmdxmZBQYExJpXDtEsr3bwalgsI1S7CbqoidNJ3ADl1RB7TcUGSrpZI9a/8PjTaQLqkX5txpQRCE1qDyU6I2BjfrGk67k7z1ORz7KYO8jTloQ3TE6XugkJQcrdnKuqIvUEpqOgUO8FLUnqvZtJmyb77FP7UvAddejVKnp2r1asy793A824G3+t3VehUqjR5LtfcLvPORvaQKAP9u8VRtP0HtzoPoenXGvGUfptXbMV4+Dv2gnqhjI/7n85R3/2xcLpmXb88gJ72W+9/ojC6gnc35FTwiit82Tid5trDPEzE/e9TuqZGe/S9zdF4K9toaHHU1qP2NlKVvoSr7AKaiEwAEJXSn8+SrCIxNJvKno8hKFwaCkWUnCfpeKOtUUFfj0b3+7NfS9z1q1+vhk9s2ybIL06F9KDQalFao2byFqNGXE3B1AhcHvc6dd3u2ONWk0FO3jPoz+0AXqyQFJUE1aJLCPLru+Sznru6nfc3lsHPii2VoQzvwz0d/5QddLEve2UNVxm70BiVPL+hGQrfjQMO5VjMCSz269zF7rWcxZqxAUijpM+o+VOq2/7T9f4k5QILQ9qklz3LzrJCNHrU7MMbzJ5UZr3amtrIAXVAUFXkHKM/ZS1VhBk6HFf+QeJIGXENYYj9C9rmLI4MpnkpLPnFBvdFrjB7f58/SNj7hcdvET09ulWg+7J5eo4wxYtq3HeO1Uwi97xrUv0ax/v7DxMee/anv0MCjHt330/hYKuy56Ho37+n5+cK58fSdIqUr1oJCgV91Lx54fAcf/LOIohfnIUkw69lExl1XA2xqcM61AZ5t0eVprn9yUQeO7DQx6f+mUNwnmmXlp2/r3Ul75weR7z0jil+hRciyzKHvX8ZR514pWlKqMMSmED/0Cowde6PWNRyKIkkKEvWeb//jbZKkIHzIRMp2raN0xyoAQlNHowkK4YGHvDs/tOCIGWQXAUGtv5J1a5JlF/k/L8RaWkDi9X8BNnP5fXFMmRVNcbaVkGgN/oaW/xVUVuqkKHsHCSkT2l3hC4g5QIIgtKhjW7+mLOvkvrcBoR2I7jaWkNgejQ73jQjoTERA59YMsYHAsYNxlJRTvXQ1AH7JiQSOGUzQpWPplpzp1XtZC/IJGjzUq9c8H1Xu3Ur5tjVEjp2K0k9L3zFG3lzbl+KcOjRaBaHRrbPDwoEv9hHZL4qoAW1/mHmjRL73iCh+hRZRW5JVX/iGdx9OZJ8JaPy9s4pfS4kcOpmazAPUFeeBQkH1sX2E9Rvt9fuo/P7oAW67i13Jskxh2g9UH9xF3OUz0cV0ANwrhvrplMSn6FstlqWL3fO3oxJ9u3q4r4ieYEEQWoq1ohZzeQ4A2oAwOg+7kYCQ83s6j75fD5zVZso/+R6Fv47anQcJHDO4Re4lqVS05VwPUJ2+l/xfFmLsO4SQQSc/MylVEtEdW6/D+dgRO6X7Sxnz8vhWu+f5RuR7z7SNcRjCeefE6vlISjWGuK7EDLz0vC98/xA7YTr6uE6odAE4LY3vb9xc2ftNAPjpfPNnMnv2bG6//XbsdnuLXN9ls1Lw6zdU7FxP9OSrMXTt2yL38dTxTAdanRG1pvUK7vOKfA5fgiAIHsj4eBt1NaXog2PpMnzmeV/4/sF/UG/0A3qijo3EVdMyub6swIrTVIPKaGyR65/NkiVLmDJlCjk5OS1yfVmWqdi9ibwln2NI6UP0pKt9uoPF8Uz3zhwhyc1bm+aCJvK9R8STX6HZ6sot7H5zI2X7i+gxawAdJnfBWl1K/LArCe8+wtfhNYkuMo6ka/5Cxrz/4LJbW+Qe2QdN6PzD0Opbf3uHJUuW8OyzzwLQr1+/0+7Leq5sVeVkzZ+Lw1xN9JTpBPdpmd70pti22YYhrKevw/AZ0RMsCII3uBwujny2jYKVRwnpE03vR8dhzq0kOLYHKSNv9XV4TaLQawm/70bKPvsB62HvDnX+Q87hWnC50HdJaZHrn0lFRQXTpk0D4P777+eHH37w6vVlp5Oc7z/GdOwgxj5DiJ50ZbNXBW+ubZtt+EcH4B/l2RotbZHI954RT36FZtvx33Vk/3YUc34NW/+9ih8u+gQATaBv9u7zBoXGj6oj+7BVnWG1hHOkDVBiMZdSnLsTp7Nlnr6ezsCBA+nVqxfh4eEMHDjQq9eWZZncHz4DoNOsv58XhS9AkFHCbj23xdPaBNETLAiCF+QsPciRT7Zhyq4g+8eDrJz+GaXbc/Dzv4BzvZ8Ge34xlr3pXr+2LtD9fKl66ybsFd7/LHEmRqOR6dOn4+/vz/jx3h8GXLJhGabj6cRfNYuYKdciKX3/LC3IKGGrseGyOX0diu+IfO8RUfwKzRY/Jqn+e12EP52v6kGXS+4lKP70qwCf7+KmzMRhqiR/5bdev3ZkonsOTPqur6guP+H1659JdHQ0e/fupbi42OvFb23uceoKsomeci2a4PNnFetpV+kpLzxEVdnxszdui0QyFATBC8IHJjT4OTQ1ln7PTCahzyU+iqj5gqaNRxGgp/i1j71+7fA4DSgUVKxeQdXmDV6//plIksRXX32FyWTi/vvv9+q1ZaeT8u3rCB04isAuPbx67ea4eKoOh8XO/s/3+ToU3xH53iOi+BWaxVppYd/7WwFImNSZS7+/gV53DSIw2ncrOXqDX0g4AKYThykp9m4v4qDLIojrNAYAnX/bm5vi66FPf3bVdB2BwQkc3fUNDnudr8NpdX8Mg2rKlyAIwv+SZZlD77oLOG14AOO/u4U+j40nZmxnFOfBU79zpdD6oe3u/rxi3ubdoikkyo+4u+4DQB16/nQIN5tCQna5kJTn1x66iR1V9LypN/s+3kPZYc+2S2xrRL73zIX7G0vwKpuH/SD7fjo5d8VpMZP73SfUldpIvO1hdFFx7P99u+CDHzzUEmF6lSWy8eOyLFN78ADKgABkh5NvXUNRlXv2S/7bo309aidXLsJfG0ZwpR9Unn6xjckhd3h0vcy/ef6U3Rbv2Vxm2abEZbXhqjbhNJmw5xZg2ZeO9dgJdL27EXrzNQDMvnUxAKZKO39fpCC48Cvun93tlOsds57mD7wR77s8WyHyv8surf/eXlaGvaAIdUwU6pCTw/Bkp5Noxw9kmDdxdMVHDAi9zOM42gSx9YEgCE10NO3kiC5ZdlGyYinlm44ROeUqjP2Hkbf75MJGhxac//k+PKKq0eOW7FLK6yoBCOuo4K3KhEbb/dlvxZ7lXMOGEgA6lXVCu8p2xraTP/+XR9fMuMOzTnNXsIfTqmwKZIcDZ40Zl8mMo6QMy4F0LAePoAzwJ+LB21H6uxeMXHj3XFwumfvXKjmwawWLPz+CUnnqIlffVA7y6NYljsCzNwLmpE2p/95ZW4stKweFvx6/hPgG7fwKMnE59rD05iX0vf45VJozfJbwLMQLi8j3HhHFr9BksixTuv43KnZsAFkm7ppZ6KIujBUePVF78ACFn7uHQMU9+AgqzTGvXl92OCmuyiA2tK9Xr3vGe8oyjqpKrHk5WI9Vo+3RCXV4CLIs16/OKMsyrioT9oISancexLxxNy7T/xTmkoQmKQHZ7sC8fhtBl01EUqmwWpzs/K2M71/PAmDKHa2/f7E1O4f8OW+D04mkUhF5zx3oOnfCaTJT9OHHWGtOIKEgRBPT6rH5miTLSLLnGa4pbQVBaNtq0vdTuupnrCVFhE+YSvCA4b4OyWscpjoO3P0BAB0fuYyA7nFAvlfvUVJ0gIDAaLRao1eveybO2lpsObnY6kpQx0ag7ZKI/Pvv9T/yvctSh72ojLqDRzGt3YGjsLjBNVSRYSj1OmxZuVj2HkLXPRlJ68ee7XW883IFu7dauf9fwY0Wvi3JZbNT8PLrOMorADBefBHGyRORXS7KF/1IzZp17uMJvZCk82sUWmsQ+d4zovgVmqxk9VLKNq0kuP9wQgaNPq/md3qD0+ReHEkZGIgmMhrwbvFr3nUEu6OWqJCWX4HYZbNRuWEN1bu3Yy8rqT8uadQoQ404istQaP2Q/DS4TLXINndPscIQgP/Qfmg6xKEI9EcZEIAqLBiFXkf18vVULlxC/j+eB+CP9aL7jgthxr86EhanbfH39b9kh4PK35YjKRUEXTSByp+XYT2RhbZTEkXvf4i9rJzBoVcS7Nf+Cl9BEIRzZT6WTt63n6BP7Ez85CvwT+zi65C8ylV38smovnOU169vq66jrOQQiZ0meP3afybLMuZdu6jesAlrZsPVq9WxkThK3AtuKQL0yHVWXLXuKUCSWoWuTw+CLhqNIjAAZaA/SmMQquAgrFm5FP7nTco+Xlh/rbuATilq3voykoHDW28P3z+YNm7GUV5B0OSJVP2aRt3RY8BEKn/5jZq160kYfAXhXYe1y8JX8JwofgWPybJM2ZbVlG1aScS4ywgdMtbXIXmd7HJRuWEt2sQkIm+82et71slOJ6ULVmIMSCBQ5/1k2+BeskzR9wswHz5AQK9UwiZfhjY2HkeiTPkXS0AGw4ShuCxWZJsNhb8eVXgwqshQ1DER4FA3el3DhBFou3XGWVGF7HBwSfR6kvoEEpfcetsL2G0u8tNNWNIzsBeXULvvALpuXan8eRn+/fsRNGYU9uISrFk5RN5xK8FL2+/WB2IYlCAITWU+cYT8RV/gn5RM3PRZSIrza36nNxR+txllgJaUF65Hl+D9Tvyjn21DkhRExfT3+rX/zLRlC6ULv0GbnEzYddfil5iIMtlI1U+rsB7NJmBEf1BIuMwWJI0GVagRVUQI6rgoFFLjHdZ+HeKIffkJ7PmFyA4HLlMtT41fw8DhWhSK1nniK8syRZm1WA5noND6Uf79YpTBRmo2bEIdE03Y9dN/f//bCBw5jIikC2t7Ta8T+d4jovgVPJb2cx3FK5YQPHAkIYPH+DqcFmE5ko69uIjwO+5FFeDZXBRPyU4XpZ+nYcspITX5thbdDN5hNlH03QJqMw4RNf0mAnv1rX/N5W8l7K7pZ4/3DK9pYqMg1l28jxp1tJnRNk1hppm5d+yjLK8O2Fl/3HLoMJJGTfgN05GUShylZQAojUagdbeUOp+Iff8EQWiK0hInufPfRxuTQMy0G9pk4euosVCWto/wy/qj7+T5ehSeKt50nBPf7iap8xQ0fgFev/4fZKeTyt/SqExbTuCQIYRNv6b+NZfCjnGqB9scnWEqsspoQGU01P88eOTW5oTbJLY6J/Pu38vh9eXA5vrjzopKAKIfvA9VSDAuiwWXuRZVUFCrxXa+EvneM6L4FTxSXeli3twadPFJRE28wtfhtJji7xaiMgajTep0zteQHU7KF62j9sAJJIUCVagB2WrHkpGLo6SSsJsnEbS/5ebFWosKyP/sQ2S7jZiZt+Of4vstp0rz6sgtqCC2l7FJRb+50s4vc0+Quaua+O4BTH0oiSWvHUdSwN8WpPLV7nEUzfsUR4l7ZcegMaOx5eVTuSyN2oOH0cTHoYmOAnJa6J1dAERPsCAIHnI6Zd6eYwJJIm76bSh1bXPUTM4HK3DWWgmf3LdZ1ylYdYTcZYdxmKzoY4JAljFlVVCVXkz4oATig0Z6J+BGuOrqKP7kUywZRzBOvgjjBO/v59tUpmoX6futODo7UWk87zRxuWTWfJbD/hUl6AwqJv8liRN7qsjYXMHNr/VkeeVlVP70C+aduwHQdIhH8tNQ9vX3mHbsBJUSfZ9ekNtCb+xCIfK9R0TxK5yVwyFzw+UllJc6iZh24e7ndyayw+Fe0CkyGmd1FbhccJZl/GuKLBz4NZey4zUcWpZH4pBw7NcmYtqWTtlXq9DEheOoqEFdbUah1+LfvwuG0X3RJcfBfpf334PLRcWG1ZSvSkMdEkLMnfejDjJ6/T5NtezjPL5+8QQAt346nIR+nq1U6XS4+PQfhzi2vYrITnrWLyzAXOWgOKuWHqNCSUoNQlMcTdQ9d2DLyaP448+o2badyrQVqCMjCL1yGgGDB5532zG0NtETLAiCp559opqvvqwlfMLUNln4yi4Zl82OLjEcSaVAdp09F9utTrYvLqQk28KunwtRaRSkPBkOEux65ld0UQbqSkw4am2o/P3QxxlJmtGPyJGdqHupZeae1h48RNmiRbhMZqLuvhNdcnKL3Kcp9myv46Fbiqg1yQy58xDD7/V8XZO1n+Ww+KUjdBsZyv6VpRzZXMHgq6IJi9eROiWSNWmhhF5/LbpePaj6bQW2vALyX3wVZBnDyOEEDh+KKtjY7otfke89I4pf4ax2b7eRleng0+/CeHFHR1+H43WO6iqynn8Ghd6fqJm3kP/e/1H4+ccEj5+INr7DKe0LDlaQsbKAnd8cx1JpQ1JKIMtkrCyAla/Xt7PluheYCpo0EOOkAS36Hpx1dRQs+ARL5hGMQ0YQOmEKCr/WXXiqMYWZtXz33yx0gUosNU6Kj9YQ3zcE6SzzhWx1Tj746wEObyjn9jd7UlVsJWtvDRNnxfPOPfvw058saNWhofWLWzgrKgkcPpTQqy5v90VvPdETLAiCB2xWmcXfW/jLgwEsDxjj63C8TpZljsxeSPWO43R97SZUQXqOPPU1sTeOJGT0qSOkaspsbFyYx4E1pWTvrUajU2CzuFAoJdbf/lV9O0tBNQD+cUb6PXtxi7+Hil9+pfK3NLTJyYTecTuaiIgWvacnHHaZ5/9RRpBRSa3JQf6eUuqqbWgNmrOe+9u7x/n5jUxGzYxj1I3xPHfRJoZdF0t1iQ2N7mQeV2g0BPRPpWr5KnA4UIVEE3nXLJSBLTes/IIj8r1HRPErABCuOH3vp8biBOCt56vIj1hK0OQJSOoL/3+diB3u92y3SmQBrlozrDoAQO3hgyhKzCSMu5/164cCUFl6lJxja6goSW9wHdnp/u2hVPnRO+kaqkx5FJTtxmKtACRCfnURv7PBKSzZ8DhKpRKV6sx/jp1ffv2Mr8uyTF3mUZj/DFKJnac/S6LX0Brg69OeE66sOeP11vxi5tBeKxt2KCk95m4b2TWIcQ/0ILq78ZRz3jxy+oXPTrz4HcrQIGIevIxjj3/O0n/v5ef/HEB2Oek86iaMcT0AGYWy4eJaOT9+TFFVNakdriPr3U4cyl+GTm1k0f1B1JTaqV41gQ9/q+F41iPk5OQQFxdH+oyZZGZmctFFF6FQ/Km3/a+nDbHNEz3BgiD8IVJx+pxjcrmw22V++7GOvIgvCLp8EqrQ4FaMruVUbw9HlmXspe6O17xPDmAvMwGQ+dJiFOqhKDQaDkVEU1NgYvMbO8ndXIDNdHK9CJvF/ZnB5ZSJG3E1IFNxZAfmohNICiWWLD0F8xs+INj8419QKBRnzfUAHd9+9Yyv20tKKV/0I5bfDnDvP4KZebcVSVp42vZayXHG6x3Ya2PNcgsr9xooOlSB3eIgIELH4FndSLkovtEpSo8du7LRa+Uv3UdWZhYD3ptJwZ2fk72lmP8bvQRkFxEpw0kYeDkupx2FStPguuaNGzh4NJOOcWNQHx3Hjw9kAJsoWTeIA0e/p1P8eBbO6MaJ3Y/www8/MG3aNMquv4W1a9cyfvx4DAZDo/G0VyLfe+bCr2CEFjdypB933O5PcbGT7T+voS7jGGG3zEAVGuLr0LxC7RdAlyE3krnjO/IzVtcfT+g5GXAXhMcP/0LusdUNzpMkBbLsQqHU4HLaCArpSFhQF8KCupAYPZwTBeuRZScxYan158iyi32Z36HVzubVV1/l4YcfPue4azPSKfvlJ2z5eXTqpePx95KJ6XjuT3trqpy8+kQpK38yERWnwtAljAEzkpAUEgd/yeWj61YTFKvDUmnHbnEQ0yOYGz86/cqKsixTs+MY/t3j0UQEoQuKxFJVhOxyd6YcXftZfVuFSgNIqPz06IIiqao8TM/YSwkLdM+9tjksWOyVHClcRcfwYQTpotlVvAGA7Oxs4uLiSElJISUl5Zzff5sleoIFQfBAgL+CF58JZvtOK8eWH8WyP4OwO2eg7dbZ16F5hSRJRF4+nbzP3seccbD+uKHfIBQa9xPK46uy+e3Rtaecq1BJuBzuX466UC3Bnfqg0voT1n0Ylcd2U51zmOgBkxucU3JgA35+DzN+/HiWL19+znHbS8so/34Jlv0HUQQE8PL7EYyapD/n6zmdMp9/YOKtl6swGBXoO/jR7eIE/ALV5O0qZeljm1n93924nDJ11Tb0wX5c+fZIIrqeviOkfNsJ1EF6VIFawkZ0pnT9UZDdnQXF6RsoObIZ2eVEUqiQFEoUKjW6oEjqygqIDOtFUvw4JEnC7jADcODoIgz+sSTEDKO43P13tWvXLqZNm0ZoaChXXNF2155pFpHvPSKKX+GsNBqJZ592965teeMqSt77lLzZL6Hv0wNt1y7o+/a64IedhMT2pDzvAGU5u9w/x/UGJA6u/QDJ6aSytOGKxgFBsfQecicZe79DoVDRNfU69wtFFgCUCjWdYk99ImqxVlBUvh+Aurq6c4pVlmWqN62ndPEitEmdiL7tDl56fGuTFpJyuWSOHLSxc0MtWcfsmGtc7NpsweGQCY1QUpjroNeMMAbPdH/oCQj1Y+kzu1EoFIy8M4Wj64vI2lZKRY4Z/Bq/hyRJxN07hew5P3L0H5+S0GsqVlM5ubt/QeMfTFjSAPwCgpEkJQ6rGRlw1NVgLsslNCCJ6OBe9ddKiR6HUqEiSBdDXIi7M6FvwpW8s/h2kpKSzunPsT1pr727giA0ze03B3D7zQGs/foWSud+TvF/P0CTlIAutTu63t3QxLXsFn0tTRMaTvDIcRQv+QYAZaAB4+CRFHz9GS67nWPHD51yztVfXULJgTK2vLmLm5ZfjSRJbP3APR9akiSCO6cS3Dn1lPOK96wCwGw2n3O8dcezKH73QyStltDpV+E/sD+jJr3XpGvkZTvYutHKwX02aqpdHNpvJ/u4g36DNOzYYiO4h5qRf+3tjnlYJZlrCzCX1jH0ru7ILpnNHxwiZ1vxGYvfjrcOY88j37L15nnEXTOAZO14Mpa744zsNhJtYDhKjc6d62UXTrsVS0U+fhoDneLH139+iQrrQ62lDFl2kRQ/FoVCRVRYb5am/ZeoqAv7/73WIvL92YniV2gSv44diHnmMUwbt2LetovyhT9Q8d2PBI4ZQdBFY1HoWn/Tc28ozdpZX/gqVH6U5+6lPHdv/etxSaOJ7jCYzINLsdZV0q3/TFRqHd3739ik+5RUZtR/P3PmTI/PM+3ZRfmyX3CaTUhqNc6aGoJGjCb0ksuQFAokaVt926oyB5t+qSQsRk3/sQYkScLpkDmyx4zF7CJ3dwVpi2vIy3Kg1Ul0TNYgSdAxWUNsBxU/f+MeChYU4+5Z3r0oi6XP7AZg4PVJuJwy+fsq6DI6ivDOgWdcSDl4bC/0ybFkv/oDx9Z/Sa+pfyem14Szvt/g3eUNftaqDfSMu7TBMUlSiMLXE7Ls/mpKe0EQ2jWlIYCIv99F3b50alZtpvqnlVR99yu6/r0wXnkR6qhwX4d4TuxVFfWFL4Czpprsd04ON47oGcbkOWPY8+lBjq/OYeiD/QjtHExo52C6TvP8CXhdZTG2Gncee/TRRz0+z1ZQSOn8r7EXFaPQaHBW1+CX2IGIu29DqW/4tNdhl1n5sxmzSebiq/zx0yqQZZkTR+2UFDnJO2ol7edadm61oVBAp2Q1en8Jg1Hi4iv0bF7n7oAPTnBv61h6tIqFs9wFe3TvUMJTgln7+h4Co/R0u+TU9U/+l3+HUAZ9cgtZX2wh+8stJAyMZuBNZx7GDRB0tLbBzwqFks4dJp7SLiEh4azXEhD53kOi+BWaTOGnwTB2BIaxI3CazFSvWEvN6vXU7tlP9OMP1g8fupAYIjpjjEpBqdYT2XkYB1f9X/1r3QfcTFhUDwB6DLz5nO/hdNo4mrsCgDFjxhAfH+/RebaSYooWfIE+pRuBg4cgW61oE5PQJ586xNda52L2jUfJy6zD5YQOXbWERWtI32nGVOUebhxgUDB8gp6Hngnk0L469mypY/t6C/oAiYJc9xwhQ7BE9o4ycnaWseXzY/XXP7q+iMwNxaRencikR3ue9mmz7HBSvf0oFav2Yzmaj62oCqVai0p7YY8QuBCJOUCCIJwLSaFA16cbuj7dkB1OzJt3UrVkBYXPvEHkP+5Gkxjn6xCbTOkfSNDAYdhKSwgZOY6SXxdjKy4EwDh0NNPmxKJQKhjyQD+GPNDvnO9TtMs9zFmpVDJt2jSPzpFlmeL35iGpVBgnjsNls6EKDSVgQCpSI3OGX326nEVf1qBQwIdzKumZ6sexdBu5WSfn+g4d5cd/Xg/GBWxcXceKXy24XFBe4qKizIVSBebSOnZ8mc76t/bjqHN/TgiM1LHk4Q3E9Q9n0uwB6ENOP6WqOr2QwmUHqNyTiyW3AgA/Q5inf1SCF4l87xlR/ArNogzwJ3jaFPwHplLwwhxM67dgGNdy+9q1FK1/CN1G3lH/84DLZmO3W7CZKwgJ9M4K17XWMlyye/GMRx55xOPznCYTyDKhUy5F86dhP7Isu7dl+t2+jTXkHKnj3ws68+SMo/B7J+CoacEYw1QMnBBEj0QbWp17QahHbikA4InXIxh3SQAqtUTOcRvff1bNbz/nUVNcVz/XqeelcWgD1ZQc0XLJU31PG6/sdHHsyfmY92Wh6xRF0PDuaBPCCM1JRe3X9rbOOO+JOUCCIDSTpFISMGIg+oF9KHruLSp/SCPiwVt9HVaTKVQqIi+7uv5n3Z0P4qipwl5WijY2HoUy4wxne648YzsA99xzD8om7DzgrK7BeOkUgsaNOuU12ems38WgzuJi8YIa7v1HMBtX1nIsw06dxUm/oVrGTFYwbKyOvr2V6P1/z/V3l7H8Fwt3/jWQG28PxBCkoLrKxaKvzHzxbTWHl2XX/+4PTzbScUQ0GWm5THpqQP2T4cac+GIzJz7eiCbUn9ChnYi9rA+GnjEoNnTz+D0LXiTyvUdE8St4hSYmCl3Pbpi37bogi98/U2sDUWsD0QdGoLB4Z09eP00QaqWOEEMSF1/s2XYIsiyjDHTPt86Z8wpBI0ah69QZ8/59WHOzsZdXIDvsfJQewvS/RpG+y4w+wD30CSAiXsOYK0KY+3gO5monRTk2Am/Wc3BXHaZqFzq9xGUzDEy6/GRyi++o4YHZYXR7uBfLXtzL0fVF3DZ/NDqDhlVvHnRv7XQGpUu2Yt6fRcdnZmDo36n+uGZhUFP/yARBEITziMJPQ8C4YVTMX4KzxnTBr/eh0GjQhIajCfXuMO7AuGRs1eU89dRTnp8ky0h+GioWLcF6IouAQf2xnsjCcjgDR1k5LpMZbbcUjne1UVXpwuWCbr3UzH3JCsCDT4byxP0lHEu3s2mNhZfeDGH/Hgu1ZheZR+x0TlFx799O5mFDkIKb7wpEce0wjq3N54e/rmfmVxMJTzaSu9O9VeOZaqOao8Wc+HQTCTMG0fHWYUjKk7ssmDc06Y9LEFqVKH4Fr/Hv34fSeV9iLy3zdSjnJY1Kz6jUR1FIyvrhwlarlSNHjtCzZ8PN4EtLS5kzZw5Zb7yB02xGUqtRBYdQvXUzVevWoAoJQZ/clcB+obisVlZ+u5x1SyqoqXAPWXrq+mMMnhTEvc/Hc//EQyT10NF3lIHPX8pnxTflKFWgVEoMH6/npvtOv4hFVLcgts3PxFpjR2fQEBzvT3WhhcLDlUR1NZ7S3lZSReGXawi9ZECDwlfwHcnl/mpKe0EQhNPR9+9FxZeLqd2xn8AxQ3wdznmp05Q7QFIQHu4uqu12O4cOHaJ3794N2jmdTubNm8dbb71F1r59SBoNCkMgtqxsinftQdL6oe/VA33P7ii0WqrXrOfGyWU43ame+28sJiJKySc/RfP8Y2VUlruY/XoYr80u5+pJRUgSqNTQtYeGx541njbeqB7u3TuKD1cS0TWYoBh/JIVE5pp8Qm46dYqV7JLJmLMcfUIIibcMbVD4Cr4j8r1nRPErABCh9Kz39r3J8077Wt0oFzd+q2Dw8fcZO8no8b1X/fYPj9t608av/+aT+wJ0/+frOGpNHHnD3Suc8siLKNQn50pnLXgHS+4JRlwXSWRSLFt/KKAyv4K/rxxMnclBSJwOhUIC3ItFWEsG8sPrJ9i0qKj+GsmjIlj6vQVTtZPp/+5OWJyWmH4RfLxpMNouHZGUSjKBv2xpPEbjJjtZazMBmP/XChLHTMHlsKMLKebTW3bS+eK70QVHEfm5exNjWZbZbV+DyqWkd1oS6uUnGlxvmeXf3vnDE5pGDIMSBOF3AQrPtsP7bNyHZ3z9sR91FG5Zyryn99Hv52qPrrnz4uc8atcS0p98yLsX7O9Zs45vuRd9yn3mBRxlZUTedze65JMLZ1WlraTix5/R9+7JtNk9yN5Vwa4l+dz++WACQv0IitKi9vtj2LQNh60/Rxfs47OXC/DTSVgtMikDAjlaqmf98lzuezGO1KmhvNo/nCeX9EPbLRGlvw4z8KQZ2HRqjJq9Wqr2uBfNXP7yQbLtMwAw9q9mzesbOHKsJ0E93W849vmNAOTIx6ihkP6MRjM575RrbnWdfbEroQWIfO8RUfwKXqPVK5h8nZGf51cycIgLSRI9gWci222gUKDU6kGWkZ0OTJnp1BXmUnviCLGX38RV/3InlW6jQvnP5E1888xhxt7aAVOFnc3f5lNbaWfSvR3p3F3LzS+kcOOzXVCqFbicMmnzcvn5nSwGXBxBWJz7A0/HPgZ0FWdfsdJ6PJtD332Jo85EePcRRPefBIBCpabz5Ds5snQuJ1Z9SdfLT36gyHMepcSVSx/1KNTShbfoWVslFsAQBMHbrr41mEdm5nFoT91pt7sTTlL466AMJLX7Y3dd5gnsxcVULl9JwNDBhM24hsHjVzDw6njyD1Xzw9MHmPy3FFxOmb0/F5Czt5J+02Lpc0kMV94VwbRZ4SiU7q2Wtq+q5j93niAsWs3Iy9wjuSJiNfgPOPu8W2eViZwFn2E+noF/p25EjLuk/rXICdNwWmop+Okr9PEdUQe5nw6b5GqOsJdYkgiWLsxVv9sqke89I4pfwauGTQrkuw/KyTq+isSk8b4Op8lqamr47bffWL16NWvWrKGmpobOnTszd+5cunTp4tV7qYNCSHn4eVw2G5X7tlG9fweW/CxQKAkZOIrArn0Ad/EbGqvjhhe78/1zGez5zT0XJzhai93q5LNH9vPszwMAUKoVZB80sfC5o2TurmbszFguu7/xLQocVdWY1m/BUVKGKjQEXZ/u+CXEYS8spuiND9AZIuhyyT34GUIbxq0PJGHUdDJ+fItDi16l3G6kVq6h1JVHnLIzkUqxJcF5RWx9IAiCl/XopyUsUsn7L5Uh/ct1wQ17dblcrFmzhlWrVrFmzRqys7MxGo3Mnj2byy+/3Ov3i37oflw2O3XpGZR8+iXmHe6tFbWdOxE87eQ2fgqFxLUv9uHbf+7ls3t3AKDRKzFEaln49z10SA2GJFCqJCpL7SyYU8SyBWX0HRnA7U/G4qc99e/BZbNjWr+HukMnUAYFoO3aAX2/FHC6KHzpcxzFVcTPuIuApIbDmyWFgqjJV1GbdYwTn7xJQOduVMvFFJGLDn+S6X3KvQQfE/neI6L4Fbyqez89194dytfvriQ6diB+fgZfh+Sx1atXc+ONN5KXl0enTp0YPXo0BoOBOXPm8NZbb/Hmm2967V72mipqMvYjKRUU/uLec1AXn0Tc1bPwT+zSYAj0H/pfEkWfiRHkp5uwW50EhGh44/rtRHZ0r6B8bGcVr1y/p779gIvDufbxU+fd2kvKqPrpN8zb9yCplKhjorDsO0TV0jSMl0/Bsu8QyqBAuky+G6Wm8SFyAZGJdL7oDsqP7qCw/CBayZ9kVT/ilcne+OMRvEj0BAuC4G06vYJ/zYnioRl5JG48TPDI7r4OyWO5ublcf/31rFu3jpCQEEaPHs2gQYOYN28ejz32mFeLX5fNTu2efbhqa6leux5HSSnq6CiCp15C4IihKLSn5tjorgbu+244ZVm1VBZYiEoJ5Iv7dxIQqkEXpKa6wsFDl2ZQVmivP2f2x0mnbD3ostqo/GEN1cu34TJZ8EuKoe7wCap+Wo+ubzJ+HaKwZheSeNMD6KIb37ZK6acl4Ya7qdixAXNmOlbMxJJEPJ1QSqKEON+IfO8Z8X+u4HXX3BXKt++byM3eQKcuU3wdjsduvPFGCgoKmDNnDuHh4Rw8eJAFCxYAMHHiqZuun6vCwkKOvv1Mg2NBvQYSc+mMs56r0ihI6OXuUHjj+u3UVjkIjvFj6f9lsXlxUYO2238uYdxNsST1bdgBUfbxAhxlFQRfMYWAYYNQ6HXITidVS5dT+cMvAITdOROlfOa5YYb4rhjiuxKWu/OscQs+JOYACYLQAlKH6Ok/XMfBbzZiHNHttPu+n2+eeuop1q1bx6OPPkr//v3Jzc1l8eLFlJeXc8MNN3jtPrIsU/jWO9iysuuPKQL8iX387FsdSpJEWKI/YYn+rJ2XSfbuSjoPC2X1B8dYX1nRoPAF+PGTUqbe2nAIcuWSdVT+uB7DhEEETR6COso9iqt2VzpFr3+FZXcGgeMGnLbw/YNfaARRk64ATs75Fc5TIt97RBS/gtcFGJTExg8lN3sDsXFD0OpOv5rw+WTcuHF8/vnnPPjggwCEhoZy8cUXM3/+fAYPHtzs6y9evJiePXvy2muvuQ8olOByEjpsIqGDRuOoNVOU9j1OSy0R4y5DGxFzxuslpgZRkl3L2s9zAfjrhz2J6eKPMdKPnctKeP+BQ5RkWxoUvwfWlWPNzCL83lvR9z7ZUy8plRinXoRCr8W8dTe6bslwsNlvudVUVFTwxRdfcM011xD1p72Q2zvREywIQku58S8hPHR9HhVrDhIypoevw/HIuHHj+Pjjj3nllVcA8PPzY9SoUSxcuJCrrrqq2dffs2cPZWVlmEymBoWvvm9vgi+dguxyUbVsObUHDxM0bjT+qX3OeL2gKC3GGB25+6s4urGMy28P5/+WdyUmUYO52smN/Q6Qud/S4JzqCgfVv27CMH4gYbdc0uA1fWoKUY/fTOm8HwkcnQq2Zr/lVuNyufj000/p3bs3/ft7uOpYOyLyvWdE8Su0iISOYyks2MmR9CX06nuzr8PxyLx587jjjjtITEwkJCQEvV7vtZ7srKwsLr/8csLDw1m4cCFf7zmKvkNnsufPpWxjGi5bHerAIKoP7kKp8yfry/+j871PnvGa0x7twrRHu7B3eQnz7t/LrrQyuo9wL0hxfE8NAN2Gn+x4yNxdzZezj+DXJQldr8YXwjBMGI1hwmivvOfWtGLFCv7617+yefNmvvzyS1+Hc34Rc4AEQWghqUP1GId3Je+DNIIGdkLp79lq0r5044030rNnT/z8/IiJiSEgIAClUnn2Ez0gyzJ9+/YF4PDhwxjGjSZg8CDyX3iF2t17cVZXY7z4Iip/+Q2Ako8/R+GvR5d8+jVF+lwcQ5+LYzCVW3lh1ErWLKlg5qPRSJLEsd+L3s69dfXtK0rszPlbNrJLJvjKMY1eU9ctkfhX7nf/sK/577u1ZGVlcdtttxEQEEB1dfUFM9qg1Yh87xFR/ApNMkJr9qhdRf9AQpUTKVj+HUXdaXQO6/lGpVIxcuTIFrn2I4+4hzmVlJQwfPhwIoZPpjpjb/3rFdvXETpwDACGlD5U7N5I2YblfHdxv7NfvBuodSdYt7CAatss/PyDKc/eB3zCP0ZuJ7rHWGJ7X8Tu759FqTZwcF0aSUlJ3ntz73vvUudq2rRpPPnkk8yYcfah4+2N6AkWBKGpBvnZz97od8Gp11C54XnKvqsgIKXn6Rte7IXAvOSPAtXbPvroo/rvs7OziRs0DZfVSv7vx6yZJ7Cs2IJC44c2Kh5rWRHV3/9K8PVJLM458xNggLBJpZQs28vjd/QnOK4H9roa4Bk+fDafRZ9F0aH/NLJ3/kh5jp0fvvuaqVOnnj3o6U14g895ecuoJurYsSPz5s0jLCxMFL6NEPneMxfW8nzCBUWpdS/E5DDX+DgS39i5cyf33HMPb775Zn2iffvtt9Fo3B0BuugEUJz8J1i2bTUAFbvdc2rKtqykam/OWe8jyzJ+Ae4nvrLLCUBwXHciU0Yguxzk70sja9v32C3VJA662ruF73lCrVbz7LPP0q3b2bd2aHfkc/gSBEHwkFLrfuroqPFsv9+2prq6mvvvv5+HHnqoPgeNGjWK8ePdO15Iag3a6JO7IFQf2o3LZqU2+yhOcw11xXmUbkzz6F4BXd3ToRxW94MItTaQxIFXAlB2YifZu36kPGcvkcnDPSt8L0C33norl112ma/DOD+JfO8RUfwKLaY2/zhKfQCqgAtnxWdveuutt3j33Xd54IEHKCsrIy0tjXvvvbf+dXWgkYSrbm/03D/+zIpXHj7jPWRZ5tjbKzGVnKDj0OloA8MAkBRKOgy8guB4dy98ybGtxPWZQmDkqas/C23bHz3BTfkSBEHwlCU3CwC/6FgfR+IbK1as4O2332bOnDncdtttrFmzhqVLl6L4vXNbkiQSrp51mrMlZKeLsq1rkJ2uM96nfH06x99aRlhif8KSBtQfj+wyjIR+7kK37PhOgqJTiOkxzivvTbiwiHzvGTHsWWgxAYkpVOzZyLFPXsbYazBB3fqjMVwYi195Q8eOHeu/j42NZcKECae0cdSaAFAbQwnqMQBz9lEkSUJSqXGYqin8aQ9dHjz9StMFS3ZTsHgXiYOvJrzToFNe7zL6Vi+8E0EQBEFonDY6DlWggdwv3sPQZwCGXgPQxsS3m2GpERER9d/37NmTUaNGnbF95MQrMB/PwGWzotBoMB09iOxwYTqUR2DP+EbPqc0q5eiLiwkd2ZWk6OlIUsNnV9FdRxHd9cz3FQTBTRS/QosJTOpO0o0PUbplBSWb0ihe9wvBfYYSPf4KJIV3Fpc4nz3xxBOMHDmSlJQUYmIaX7nZP6EzMZdcD7JM/s8LGrwmqdRETEhp9DwAl81B1mcbiZzck4iwoV6NXWhDXLL7qyntBUEQPKQKNJB4998pW7+c6n07qNy6Hm1cIrHX3IIqsO2P/Bo+fDj79+/HZrORmpraaBulVk/MxdehCQnnxBdvNXxN54+kUhPQ7fRPznM/X4cm3EDS3y5B+koM2hROQ+R7j4h/QUKL0obHEHfpTFLueYaIkRdTsWcj5qwjvg6rVSgUCsaOHXvawhfcQ5+NPQZQuXfL70ckVIFGNCERoFDQ4ZYRpz23fEsmjioLsdcMOG0bQRBzgARBaGlKnZ6IiVPp9OBsYmfcjrUwj/JNq3wdVqvp0aPHaQtf+H07wd6DMGcfdf+s1qDUB6CNjsdpMRM18QokZeMfye1VtVRuPkLk1P4oNOKZlXAGIt97RBS/gteZzKfOW1H6aQlJHQ5AbWE2cjtdXv10/hj+7BcWiSGlN7byYqInXoVfWMBpzylYshtDjxj8O4S1VpjCBUiiiXOAfB2wIAgXhNpaF64/PTmSFAoCunTHLzIGa2EestPho+jOT85a90JVst1GSP8RWEuLCOrRH0Nyr9OeU/LbXlAoCBt3YeyjLPiOyPeeEcWv0GyyLGOxuJj/jYn47rlEdcnlwH8fJv+3r+uLOgClRoux5yBKNvzKsU9epip9t++CPs/EXjKDyHHTcNqslG9fS/jwizD2OP0TXYfZSuWeHCImdG/FKIUL0h/7/jXlSxAEoREul8yaDRYuubaQsM7ZBMRlkfP5O9TlN9yZwDhwOLVZxzj2+jOUrv4V2en0UcTnl7BhEwgfOYXAbn0pWfcr+thEoidfc8ZzKjYdxTggCXWQvpWiFC5YIt97RIyfEJpEJ/k1+Lmk1EFUr+ONtq3Yu5naknSSnrwKbYdwHNW1JA4fjWl/EsVLtpP742cokkoB3+4b5wsRu21/OhINqmjW1PxIUpcpJPiPht02KmobHzJdlbkfXDLK0gFU/xDB7s/b35+h4Bmx758gCE3lJ6lPOTb5ujzS1tSecrz2+BGyPnydjv+8EuPQZFx1NrS9owmefDulv+ym5KfluMhBlv/WbhbB+oO29M9HAghImcjREx8QGNOFzuPvQqpy/5lUboo45XynpRZzegHhE6fWv374S5Hvhca1Rr6fO3cur7zyCgUFBfTo0YM5c+YwcuTIRtt+//33vPPOO+zevRur1UqPHj14+umnueiii5p+Yy8Sxa/QLJIk0T1Zw8EMG4nxKv75YAiXTfJnwBtJ5H24AmtBBYfu/RAUErhkJJWSkHE9ib97IgUGHXkfrcT5qhOlsu0vgNUYm9VEVVUW4RHu4UxKlRan88+F8alMeUfQGELQBp+aLAWhgabO6xHFryAIjejWRVNf/M5+JIRbrzOQnedg1LRcAI4//319rgcI6JVAzMxR6LtEk/XfJWzbto1Bg07dlaA9kF1OKo7vwdihJwqVBqVGi62m/KydAZacE8hOJ/5durZSpMIFrYXz/cKFC3nwwQeZO3cuw4cP57333mPKlCkcPHiQhISEU9qvXbuWiRMn8vzzz2M0Gvn444+57LLL2LJlyxnnyLc0UfwKzRIWqmTfmg6nHI+YOoDaY4VUrDrgPvB7MpQdTsrS9lCWtrd+uEVpaSmRkZGtFvP5QpZdbFzzHAAjxj2NUumHhERJ0T46JI3DVFPAoc8XoPTT0/Hi21AHBNWfKynVokgRPCLJMlIThjY1pa0gCO3H6/8O5/V/hzc4Fh+rJu7OCeR/tgZXnb3B6rGmfdlk/P0L1OHuFZ/T09PbbfGbtXYh5Ue303HcTQQn9QUkzCU51FWVoNYFcnTZB9isVUReeg3+nU7u8iCpfv+YLn4tCx5o6Xz/2muvMWvWLG6//XYA5syZw7Jly3jnnXd44YUXTmk/Z86cBj8///zzLF68mB9//NGnxa+Y8yu0CEmlpOOj0+jx8b3EzhqH0t8PSfM/v8RlGWWQDuPwroSFtc8Fmxx2CwAGYwdUKq37KXqf66k1F1NStI+Mg99jqyrDUpxDbcnJ+VSy7KLmxEF04affFkEQ6rnO4UsQBMFDEZcPotfn95P492loE8LcT3//h6PSjD4l5rRDI9sDS0UBAEEJ7nU64gZPQ603ULh7OYW70zAXHcdeWY4pfX+D88wZB1Ho9KiNwa0es3ABOsd8X11d3eDLarWecmmbzcaOHTuYNGlSg+OTJk1i48aNnoXnclFTU0NISMg5vT1vEcWv0KL8Io1EXjWErm/fjjokAIVegzJQC4CzykLlhsM42+lCGGqNPyPHP0u/QfcA4HTaqalyDx+rNRdjqsmvb2tI6EZF+nZyVnxFdtp86soLCes72idxCxeWP3qCm/J1LubOnUvHjh3RarX079+fdevWnbbt999/z8SJEwkPD8dgMDB06FCWLVt2rm9REAQfU/prCRnTg65v3UbQ4C6gkFCHBQIg253UpueTkZHh4yh9J+Wyv5I6678oVBpkWcZUlInstANQfmxXfTvjwOHU5edQ8MMCSlYspWLLWoIHDkdSioGawtmda76Pj48nKCio/quxp7ilpaU4nc5TRmpGRkZSWFjoUXyvvvoqZrOZa6+9tvlvthnEvyahVfhFBpHy35vIm7eS8lX7UYcFYhjUGeOgLmg0Gl+H5zNK5cn3XlVxnMwjvwAQGtaVrMxV6MJjsJTkYasuI3v5VyC7AImA2M74R3f0UdTCBaUV5vy2lXlAgiA0j0KtIunxKyj8ZhNF325GoVVjGNyFwB7xjB071tfh+YxCdXIBMdnl5PiKTwEI7tgHc3EWupAYLOUF1B4/ivnIQcxHDyOp1Ci0OoyDRvgqbOFCc475PicnB4PBUH/Yz8/vNCdwyjx1WZY9WshuwYIFPP300yxevJiICN+uVyOKX6HVqEMCSHxkKpFXDSFrzlKqNmUQPUP8Uv+DMaQT/gFRmE2F7N7xISBjKckDwJR3DKXGD11EPEmX3QGS1O5WzRTOX21lHpAgCM0nqZREzxhB+MX9yJu3krLlewkd3wu1+tQVpNsjhVJFeI+RlBxYx7G0jxpsN2MrK8Zls4JCQed/PIekUCAp2ueCoELrMRgMDYrfxoSFhaFUKk95yltcXHzWdXsWLlzIrFmz+Oabb5gwYUKz420uMexZaHW6jhF0etq9r13+J6t9G8x5RKFQ0n/I/QwY+lc6dBxLYFB8/Wu5q79BpQskZvhUJIUSSRL/dAUPneO+f57MAYK2NQ9IEATvUQXpSXjgEgz9ksh56xfsdruvQzpvxA25nB7TnyBhxLUY4rvXH6/cup66wnyir7gBhUotCl+haVpwn1+NRkP//v1JS0trcDwtLY1hw4ad9rwFCxZwyy23MH/+fC655JJzfmveJJ78Ck3i8nA1nLJc41laGAkYM4qKn9Kora1Fr29fm7evWvYPj9oFBARgNpv517/+xeOPP46/v38LRya0Nee67198fHyD47Nnz+bpp58+pX1bmgckCIKbp7keoOLImTutAsZOpfq/c1i1atUpnWRt3Y4PPNuTd+bMmXzxxRfccMMNPP3003Tu3LmFIxPaopbe5/fhhx9m5syZDBgwgKFDh/L++++TnZ3N3XffDcDjjz9OXl4en332GeAufG+66SbeeOMNhgwZUv+ZQKfTERQUdNr7tDRR/Aqtqu7IcWqWr8OWk4+jshpcLoqKiujYUcxfbUxGRgZGoxG1Ws2LL77IlClTGDBggK/DEi4kTezd/aNtU+YAQduYByQIgnfYy8upWrGaumPHcZSXA5Cfn3+Ws9qvt99+m1dffZWIiAi++uorNm3axMyZM30dlnChOcd876np06dTVlbGs88+S0FBAT179uTnn3+mQwf3lqcFBQVkZ2fXt3/vvfdwOBz85S9/4S9/+Uv98ZtvvplPPvmkSff2JlH8Cq3Caa6lOm0t1b+sQh0bhb5fT5Qhweh6dxOF7xnExMQA7jmVTz31FOnp6XzxxRc+jkq4kEgu91dT2oNnc4Cgbc0DEgSheWSnE/OuPZR9twgUSvz79EI1sB/ajoncfPPNvg7vvPXHU7A9e/YwY8YMAFH8Ck12rvm+Ke69917uvffeRl/7c0G7evXqpt+gFYjiV2hRrloLlT+mYVq/FdnpJOiS8QRdOgFJIeasNsU333wDIIZCCU3Xwj3B/zsP6Iorrqg/npaWxrRp00573oIFC7jttttYsGDBeTMPSBCEcyPLMjXrNlC1ag2O8gr0vXoQdv11KPW6+jZikcaz+2M+5cCBA30ciXBBauF831aI4lfwKlmWsdvdvb+SUknZF99h2Z+OYcJIAscMQxkU6OsQLwhpaWmYTCaGDh1KeXk5mzdvBqhfTVcQPNYKWx21lXlAgiB4zm6X6z87mzZvpey7H/Dv34+IWbfgFxfr2+AuEIcOHeLQoUN06NCB3r178+ijjwLw0EOezRUWhAZaId+3BaL4FbzCapX58MtqHvxXqfuA4l+oQow4SssJvXU6qtBgrFk5+HVKROnfvha3aqq0tLT6RUFUKhWPP/44AGPGjCEuLs6XoQkXoP/dyN7T9k3VVuYBCYJwZrIss2xlLf94toyDGe7Vm5XG53DW1ODfvx/GSeOxFxWj0GpRh4X6ONrzm8lkYuDAgZjNZgCee+65+tf+dxSNIHiqNfJ9WyCKX8Eronsep8Z08h+R/+BUkGU0SQnUrNmELdP9wVfSagm98Ur8B/Wtb+vpwjjtRdeuXVGpVDgcDhwOB9deey3du3dnxAixJ7JwDlppGFRbmAckCMKZPf96BU+/UlH/c7cuavKie+KoqkK22ch78b/u3yGShGHUCEIuv6x+mpPI9Q35+/vTuXNn9u3bh8vlIi4uju+//56EhAS0Wq2vwxMuRGLYs0dE8St4xYtPhrJ2Ux3jRur45KsaNm3eWf+PSh0fQ8SDt6MKD6Vy8TJKP1qALLsIGNwPy/50FAoFBoOB6667jpdeegmj0ejbN+Nj8fHxLF26lJUrVzJ58mR69uxJz549fR2WcKGSoQm7lrTbYVCCIJzd8ME6rr7MxoC+fuTmO3n7oyo4sh4ARYA/oVdfgb5XT8w7d1G++CdcVith069GtloJDg6mqqqKiy++mBdeeIHevXv7+N34liRJfPPNN8ybN4+UlBRmzpyJQqyHIjSHyPcekWS5eWV/dXU1QUFBVGQkYQgUm3ELUFbuZMi7SagC/PCLMBDYPRZJ4e7tlV0yR1/7leK0A0Rf1hdNaABZ89bVn9ulSxcyMjJ8Fbog+MQfv0erqqo8WmG5Kdccl/oYKqXnTxEczjpW7nrRq7EIFz6R64U/c7lkvvi2huey+6EJDcDQPQal/uSWaCXLD3DklaUED0oieEhnMt/4rcH5mZmZYrcHoV1piVz/v9cV+d4z4smv4HWhIUqip6Y2+pqkkOj88GTUQXpK16ZjLawCIDU1lczMTI4cOcLcuXNPO3xSEIQmkmniMKgWi0QQhDZEoZC46VoDH+zr1ejr4RN6IKkU5H61pb7wDQwMJDY2lsOHDzNr1ixWrlzZmiELQtsm8r1HRPErtDpJIZF4x2hCRyaz9373nrW7du3il19+4e2336Zr164+jlAQBEEQhOYKG9ONoNQO7LnnE2ylJmpqarj55pspKioiOjra1+EJgtAOieJXaHWyS6ZkxUGOvPwzAJdddhk//vgjX331FT/99JOPoxOENkYsgCEIgo+YMgrZe597i7PevXuzd+9eZs+ejdVq9XFkgtAGiXzvEVH8Cq3KZXOw6ZLXGxw7dOgQ6enp7X6hK0FoES6gKQusNmWxDEEQhNM4/OwPlK8/uYbH3r172bZtGwEBAT6MShDaMJHvPSKKX6FVuawOtDFGXHYniXeMxpJbTs2yYyQnJ/s6NEFok8S+f4Ig+ILaqAcg/qYRGHrEcuAfC9FoNGJqkyC0EJHvPSOKX6FVKfUaIi/pQ11+BRXbjlOXW06fLl18HZYgtF1iGJQgCD5g7JeIpFRQe7wES04ZAJ06dfJxVILQhol87xFR/ApNYpXtnrWzN/6/VsHXm8j7ZA2SSoFCp8FZU0fnWy7yZoiCIPwvkQwFQWiiapfF87aWU7dWqT2SR+azPwCgjjBiL64kPDwcf39/b4UoCMKfiXzvEVH8Cq1KGxuCws/9v13cLWOo2ZfNrFmzfByVILRhIhkKgtDKlHotmthQbHllGMf2xlldyzOX3ebrsAShbRP53iMKXwcgtC/Bw1NQ6DS4rA5qjxeT9PdpjBgxwtdhCULb5TqHL0EQhGbwiw3FMMQ9t7fk2/XE3H0x99xzj4+jEoQ2TuR7j4jiV2h1htSOAOjiQ30ciSC0fX8sgNGUL0EQhOby79kBAF0nsZ+vILQGke89I4Y9C62qZl825asOoEuKJGRcT1+HIwhtnxgGJQhCK3NabOS++j2SRkX0HZN9HY4gtA8i33tEPPkVWpXL7gQg7raxqPxPXSSjPdm2bRv33HMPBQUFvg5FaMtcctO/BEEQmkOWkR0ugsf3RZ8c6+tofKqoqIi//e1vrFq1ytehCG2dyPceEU9+hVZhLapEUigwpCai6xBO8eJtBPXr6OuwWp3dbmf+/Pn89ttvzJ8/H4Du3btz//33+zgyoc0SPcGCILQSp6kOe1k12g4RBE/uR8WynUTdMgGFVuPr0FrdypUr+eGHH3jrrbcASE9PZ+zYsT6OSmjTRL73iCh+hRZXmraXE68vRVIrCRnTA0tWCZasEmRZRpIkX4fXaoqKiujWrRsVFRX1x+666y5mzJjhw6gEQRAEofnqsks49tB7yA4XxnF9qFy5B4DajDwCerefzm6n08kNN9zAwoUL64917dqV5557zodRCYLwB1H8Ck3yVkVXj9qpPnMvZuV02Mj7aS0KpRqX3U7FqsPE9piIRheE+vMwd+MpLRXt+WXr1q0NCt+nnnqKZ555xocRCe1DE3uCaZ89wYIgnPRmearHbdUq93Sm3IWrkVRKZIeLypV7CJ2ciibSiKFrJMrf27QHZrO5QeE7YsQIVq5ciVqt9mFUQvsg8r0nRPErtAibpZrCjHWYK/JxWGuRZXfi63vJY2h0Bh9H5xuTJ59c9OPKK6/kqaee8mE0QrshhkEJgtBCnA4XxYu2YMkqoXL9ofrjCQ9fRuj43j6MzHcMBgOpqans2rWLxMREvv32W1H4Cq1D5HuPiOJXaBG1lfnkH3Iv7pA08Fpk2Yl/cGy7LHx37drF8ePHmThxIlVVVaSnp5OamopSqfR1aEJ74JJpUu9uO10AQxCEpnNYXeR9uByAsEsHENArAUmpIGhQFx9H1voqKyuZP38+V199NVu3bmXXrl2kpKRgMLS/zz2Cj4h87xFR/AotwmapBiC+9xQiOg3ycTS+9cQTT/Dzzz/z3HPP8a9//YuBAwf6OiShPZFd7q+mtBcEQfBARW4tAAG9OhB/z0U+jsa3FixYwF/+8hcWLlzImjVrRK4XWp/I9x4RWx0JXldTYiVz69eEdxxETDexsmFycjIAmZmZPo5EaJf+GAbVlC9BEISzkGWZRf/ahbZDOB3/dZWvw/G5xMREAI4cOeLbQIT2S+R7j4gnv4LXLX01HYDYHuORJNG/8p///IfRo0eLLQ4E3xDDoARBaAH7f8mnML2ahAcuQRWo83U4Pjd58mTS0tLo0KGDr0MR2iuR7z0iil/B67QBKhQqDdqAUF+Hcl7Q6/Vcfvnlvg5DaK/EAhiCILQAtda9boWuU5SPIzk/SJLEhAkTfB2G0J6JfO8RUfwKXpV3sIrdS/MJievj61AEQQB3J3CTkmGLRSIIQhthqbKx9oMjGGN0aOPDfB2OIAgg8r2HRPErAFDoNHnU7p2do077mvVEHkUvfYwqIoojO5ZjNBq9FJ0gCOdM9AQLgvA7T3P991mn78C2V5rJeuFb6rKtJD07g92Xv+it8ARBaA6R7z0iil/BK2p3HqJ4zheo4yKJfOQWUfgKwvnC5QKasKKjq32u/igIwtnZy2pIv/8DZKeTpNnXoe8S4+uQBEH4g8j3HhHFr9BsTlMtpfMWoeuTTMQDNyKp2vf+tS6XC4VCLPQlCIIgtB2yLJPz9lIkpYKU/7sTdXCAr0PyKZHrBeHCJP7VCk1m2X8U04ZdZM2aTV1GFtW/rEe22gm97Yp2XfiWlpYSGBhIcHAwrnbamyach8TWB4IgnIO67BKqtmRw8JY3KP52I+b92dRsO0rcPZPbdeHrdDqZNm0aSqWSjIwMX4cjCCeJfO8RUfwKTVb00jxK3/0G2WbHtGEXtTsOou/fHVWwwdeh+cRbb73FjBkzSE1NxWQyUV1dTW1tra/DEs7RwYMHkSSJTz75xNeheIdIhoIgnIOCT1dx4t9fYy+toeirdVRuOIQqOADD0BRfh+YTa9eu5corr2TkyJEsWbIEgMOHD/s4KqE5unXrxtSpU5HbSt4T+d4jovgVmixgVP/6700rt2LPK0bXu4sPI/KtJ598kq+++orc3FwA/va3vxEQ0H57xS900dHRALz33ns+jsRLXHLTvwRBaPdCxveu/95VZ6fsp+0E9k9CkiQfRuU77777LosWLWLTpk0ADBgwgEmTJvk4KqE5amtr+fHHH7HZbL4OxTtEvveImPMrNFnIjZeijo2gYsEvaLsnETR1DLoenX0dVquzWCzcd999VFVVARAWFsabb77JjBkzfByZ0BzBwcFkZ2fj5+fn61C8QpZdyLLnw/Cb0lYQhLbLMCSFjrOvI+vl7wGIuWNSg4K4PZkzZw4LFiyo//nhhx/mxRdfRK1W+zAqobkOHTpEVlaWyPftjCh+BQB+NnfyqF3MYg2gwVY3iO38gqYYOh3tDkf/1PBGr4d43tm/fz/z5s0DICQkhOzsbHQ6nY+jErwhPj7e1yF4j9zE3t12OgxKENqD1RbPfrfVpAe7v9EbkFRLcZpMqCKGYDraSI67xIsBnqeefPLJ+u+PHTtGUlKSD6MRvEWv19OtWzdfh+E9It97RBS/gkdkWa4f6lRVfIT0TZ+h0ugJjWufvcAAn376af33X331lSh8hfOTLNOknezbaTIUBKFhrndUVlL40Uc4zWYCBgxA0mh8HJ1vLFmyBJPJvT/yf/7zH1H4Cucvke89Iopf4ax+fi+HRa9l0XtsMEVZTooyNxEU0YXkwTei9vP3dXg+s2LFCgAmTZrExIkTfRyNIJyGywVSE4Y2tdNhUILQ3h3dbWL2NQfRByrRDAmicvlylAEBxD74IH6xsb4Oz2dWrVoFgE6n48EHH/RtMIJwJiLfe0QUv8JZ9R4TwqLXsti7qgJwL/SgVPm168IX4JlnnmH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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "lon_bounds = (105, 145)\n", + "lat_bounds = (25, 58)\n", + "fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(12,8))\n", + "axs=axs.flatten()\n", + "\n", + "fv_t232.landfrac \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(ax=axs[0]) \n", + "axs[0].set_title('raw destination grid') ;\n", + "\n", + "ds_out_con.landfrac \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(ax=axs[1]) \n", + "axs[1].set_title('conservative remapped, no mask')\n", + "\n", + "ds_out_con.landfrac.where(fv_t232.landfrac>0) \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(ax=axs[2]) \n", + "axs[2].set_title('conservative remapped, destination mask')\n", + "\n", + "ds_out_bilin.landfrac \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(ax=axs[3]) \n", + "axs[3].set_title('bilinear remapped, destination mask')\n", + "\n", + "for a in axs:\n", + " a.coastlines() ;" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f4c5ceb6-e10d-410b-b4e6-193afe90e56f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "768.7117\n", + "766.0552\n" + ] + } + ], + "source": [ + "print(ds_out_con.landfrac.sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1])).sum().values)\n", + "print(ds_out_con.landfrac.where(fv_t232.landfrac>0) \\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1])).sum().values)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7edf1061-927a-45b2-b454-88cbb18ddc3d", + "metadata": {}, + "outputs": [], + "source": [ + "# look a grid structure for ne30\n", + "#projection = ccrs.PlateCarree()\n", + "#ds0[\"area\"].plot.polygons(projection=projection)" + ] + }, + { + "cell_type": "markdown", + "id": "eb590654-1ee0-4dc7-9de5-7e3274c4b38e", + "metadata": {}, + "source": [ + "------------\n", + "### Check global sums\n", + "----------" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "358e9579-c679-4907-9f7e-c1f59b4707cc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "source, ne30 land area = 1790.2597119999998 1e6 km2\n", + "destination, f09_t232 land area = 149.189408\n", + "conservative regridded land area = 149.18937599999998 1e6 km2\n", + "bilinear regridded land area = 151.03866439950346 1e6 km2\n", + "\n", + "orig ne30 GPP = 104.964 Pg C, globally\n", + "conservative regridded GPP, t232 landfrac = 104.92\n", + "conservative regridded GPP, regridded landfrac = 104.963\n", + "bilinear regridded GPP, t232 landfrac = 105.007\n" + ] + } + ], + "source": [ + "# Not the right way to calculate annual mean from monthly climo, but it works\n", + "\n", + "spy = 3600 * 24 * 365\n", + "km2_m2 = 1e6\n", + "g_Pg = 1e-15\n", + "\n", + "print('source, ne30 land area = ' + str(((ds_bilin.area * ds_bilin.landfrac).sum()*1e-6).values)+ ' 1e6 km2')\n", + "print('destination, f09_t232 land area = ' + str(((fv_t232.area * fv_t232.landfrac).sum()*1e-6).values))\n", + "print('conservative regridded land area = ' + str(((fv_t232.area * ds_out_con.landfrac).sum()*1e-6).values)+ ' 1e6 km2')\n", + "print('bilinear regridded land area = ' + str(((fv_t232.area * ds_out_bilin.landfrac).sum()*1e-6).values)+ ' 1e6 km2')\n", + "print()\n", + "\n", + "GPP_sum = ((ds_bilin.GPP * ds_bilin.area * ds_bilin.landfrac).mean('time') * spy * km2_m2).sum() * g_Pg\n", + "GPP_sum_regrid1 = ((ds_out_con.GPP * fv_t232.area * fv_t232.landfrac).mean('time') * spy * km2_m2).sum() * g_Pg\n", + "GPP_sum_regrid1B = ((ds_out_con.GPP * fv_t232.area * ds_out_con.landfrac).mean('time') * spy * km2_m2).sum() * g_Pg\n", + "GPP_sum_regrid2 = ((ds_out_bilin.GPP * fv_t232.area * fv_t232.landfrac).mean('time') * spy * km2_m2).sum() * g_Pg\n", + "\n", + "print('orig ne30 GPP = ' + str(np.round(GPP_sum.values,3))+ ' Pg C, globally')\n", + "print('conservative regridded GPP, t232 landfrac = ' + str(np.round(GPP_sum_regrid1.values,3)))\n", + "print('conservative regridded GPP, regridded landfrac = ' + str(np.round(GPP_sum_regrid1B.values,3)))\n", + "print('bilinear regridded GPP, t232 landfrac = ' + str(np.round(GPP_sum_regrid2.values,3)))\n", + "\n", + "# best results when using regridded flux and destination grid area and landfrac" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a3a301cf-fb84-4fad-821a-fa991b79aebb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    <xarray.Dataset> Size: 6MB\n",
+       "Dimensions:   (time: 12, lat: 192, lon: 288)\n",
+       "Coordinates:\n",
+       "  * time      (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n",
+       "  * lat       (lat) float32 768B -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
+       "  * lon       (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
+       "Data variables:\n",
+       "    GPP       (time, lat, lon) float32 3MB 0.0 0.0 0.0 0.0 ... nan nan nan nan\n",
+       "    area      (lat, lon) float32 221kB 1.236e+04 1.236e+04 1.236e+04 ... nan nan\n",
+       "    landfrac  (lat, lon) float32 221kB 1.0 1.0 1.0 1.0 1.0 ... nan nan nan nan\n",
+       "    landmask  (lat, lon) float64 442kB 1.0 1.0 1.0 1.0 1.0 ... nan nan nan nan\n",
+       "    test      (time, lat, lon) float32 3MB 1.0 1.0 1.0 1.0 ... nan nan nan nan\n",
+       "Attributes:\n",
+       "    regrid_method:  coservative
    " + ], + "text/plain": [ + " Size: 6MB\n", + "Dimensions: (time: 12, lat: 192, lon: 288)\n", + "Coordinates:\n", + " * time (time) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12\n", + " * lat (lat) float32 768B -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n", + " * lon (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n", + "Data variables:\n", + " GPP (time, lat, lon) float32 3MB 0.0 0.0 0.0 0.0 ... nan nan nan nan\n", + " area (lat, lon) float32 221kB 1.236e+04 1.236e+04 1.236e+04 ... nan nan\n", + " landfrac (lat, lon) float32 221kB 1.0 1.0 1.0 1.0 1.0 ... nan nan nan nan\n", + " landmask (lat, lon) float64 442kB 1.0 1.0 1.0 1.0 1.0 ... nan nan nan nan\n", + " test (time, lat, lon) float32 3MB 1.0 1.0 1.0 1.0 ... nan nan nan nan\n", + "Attributes:\n", + " regrid_method: coservative" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_out_con" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check to see if you get appropriate test values around the coast\n", + "# should be identically 1, but maybe this is within rounding errors for single precision data?\n", + "\n", + "fig, axs = plt.subplots(nrows=1,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(15,3))\n", + "axs=axs.flatten()\n", + "ds_out_con.test.isel(time=0).plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[0])\n", + "ds_out_bilin.test.isel(time=0).plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[1])\n", + "axs[0].set_title('conservative test')\n", + "axs[1].set_title('bilinear test') ;\n", + "print(ds_out_con.test.min().values)\n", + "ds_out_bilin.test.max().values" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c194849b-a9aa-4125-a579-a814dfc36d23", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fv_t232.area.plot() ;" + ] + }, + { + "cell_type": "markdown", + "id": "c70c59f8-562f-4bf8-b808-78f31a74f15b", + "metadata": {}, + "source": [ + "----\n", + "### Make plots\n", + "---\n", + "\n", + "First we'll look at ocean masks and regridded data\n", + "Using the correct destination land mask gives nicer coastlines " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0898c0c8-56bb-4880-a515-bfd091a82007", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'bilinear remapping, dest mask')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the figure and each axis for the 3 rows and 3 columns\n", + "fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(16,7))\n", + "\n", + "# axs is a 2 dimensional array of `GeoAxes`. We will flatten it into a 1-D array\n", + "axs=axs.flatten()\n", + "\n", + "(fv_t232.area*ds_out_con.landfrac).plot(ax=axs[0],vmin=0, vmax=0, cmap='viridis_r') \n", + "axs[0].set_title('area * remapped landfrac')\n", + "\n", + "\n", + "ds_out_con.GPP.mean('time').plot(ax=axs[1],vmin=0, vmax=1e-4)\n", + "axs[1].set_title('conservative remapping, no mask')\n", + "\n", + "ds_out_con.GPP.mean('time').where(fv_t232.landfrac>0).plot(ax=axs[2],vmin=0, vmax=1e-4)\n", + "axs[2].set_title('conservative remapping, dest mask')\n", + "\n", + "# Mask out coasts based on land mask\n", + "ds_out_bilin.GPP.mean('time').plot(ax=axs[3],vmin=0, vmax=1e-4)\n", + "axs[3].set_title('bilinear remapping, dest mask')\n", + "\n", + "#for a in axs:\n", + "# a.coastlines() ;" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b251911d-2f91-4207-b3ac-aab7c519cd74", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Look at raw data too using uxarray\n", + "transform = ccrs.PlateCarree()\n", + "projection = ccrs.PlateCarree()\n", + "\n", + "#projection = ccrs.Orthographic(central_latitude=90)\n", + "# TODO, calculate time mean with correct weights\n", + "dc = ds0[\"GPP\"].mean('time').to_polycollection(projection=projection, override=True)\n", + "dc.set_antialiased(False)\n", + "dc.set_transform(transform)\n", + "dc.set_antialiased(False)\n", + "dc.set_clim(vmin=0, vmax=1e-4)\n", + "\n", + "fig, ax = plt.subplots(\n", + " 1,\n", + " 1,\n", + " figsize=(5, 5),\n", + " facecolor=\"w\",\n", + " constrained_layout=True,\n", + " subplot_kw=dict(projection=projection),\n", + ")\n", + "\n", + "# add geographic features\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "\n", + "ax.add_collection(dc)\n", + "ax.set_global()\n", + "cbar = plt.colorbar(dc, ax=ax, orientation='horizontal', pad=0.05, shrink=0.8)\n", + "cbar.set_label('Data Values')\n", + "\n", + "plt.title(\"ne30 w/ uxarray\") ;" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "eaa75249-1414-44d5-b061-c98ad3f566d4", + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Data Variable must be 1-dimensional, with shape 48600 for face-centered data.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[18], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m dc2\u001b[38;5;241m.\u001b[39mset_transform(transform)\n\u001b[1;32m 9\u001b[0m dc2\u001b[38;5;241m.\u001b[39mset_clim(vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-4\u001b[39m)\n\u001b[0;32m---> 11\u001b[0m dc1 \u001b[38;5;241m=\u001b[39m \u001b[43mds0\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43marea\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_polycollection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprojection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprojection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moverride\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 12\u001b[0m dc1\u001b[38;5;241m.\u001b[39mset_antialiased(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 13\u001b[0m dc0\u001b[38;5;241m.\u001b[39mset_transform(transform)\n", + "File \u001b[0;32m/glade/u/apps/opt/conda/envs/npl-2024b/lib/python3.11/site-packages/uxarray/core/dataarray.py:213\u001b[0m, in \u001b[0;36mUxDataArray.to_polycollection\u001b[0;34m(self, periodic_elements, projection, return_indices, cache, override)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;66;03m# data is multidimensional, must be a 1D slice\u001b[39;00m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 214\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mData Variable must be 1-dimensional, with shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muxgrid\u001b[38;5;241m.\u001b[39mn_face\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor face-centered data.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 216\u001b[0m )\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_face_centered():\n\u001b[1;32m 219\u001b[0m poly_collection, corrected_to_original_faces \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muxgrid\u001b[38;5;241m.\u001b[39mto_polycollection(\n\u001b[1;32m 221\u001b[0m override\u001b[38;5;241m=\u001b[39moverride,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 226\u001b[0m )\n\u001b[1;32m 227\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: Data Variable must be 1-dimensional, with shape 48600 for face-centered data." + ] + } + ], + "source": [ + "## Sample subplots with uxarray!\n", + "dc0 = ds0[\"GPP\"].mean('time').to_polycollection(projection=projection, override=True)\n", + "dc0.set_antialiased(False)\n", + "dc0.set_transform(transform)\n", + "dc0.set_clim(vmin=0, vmax=1e-4)\n", + "dc2 = ds0[\"GPP\"].mean('time').to_polycollection(projection=projection, override=True)\n", + "dc2.set_antialiased(False)\n", + "dc2.set_transform(transform)\n", + "dc2.set_clim(vmin=0, vmax=1e-4)\n", + "\n", + "dc1 = ds0[\"area\"].to_polycollection(projection=projection, override=True)\n", + "dc1.set_antialiased(False)\n", + "dc0.set_transform(transform)\n", + "\n", + "fig, axs = plt.subplots(\n", + " 2,\n", + " 2,\n", + " figsize=(16, 8),\n", + " facecolor=\"w\",\n", + " constrained_layout=True,\n", + " subplot_kw=dict(projection=projection),\n", + ")\n", + "axs=axs.flatten()\n", + "\n", + "axs[0].add_collection(dc0)\n", + "axs[0].set_title(ds0.GPP.attrs['long_name']) ;\n", + "\n", + "axs[1].add_collection(dc1)\n", + "axs[1].set_title(ds0.area.attrs['long_name']) ;\n", + "\n", + "axs[2].add_collection(dc2)\n", + "axs[2].set_title(ds0.GPP.attrs['long_name']) ;\n", + "\n", + "cbar1 = plt.colorbar(dc1, ax=axs[1], orientation='vertical', pad=0.05, shrink=0.8)\n", + "cbar1.set_label(ds0.area.attrs['units'])\n", + "cbar2 = plt.colorbar(dc2, ax=axs[2], orientation='horizontal', pad=0.05, shrink=0.8)\n", + "cbar2.set_label(ds0.GPP.attrs['units'])\n", + "\n", + "for a in axs:\n", + " a.set_global()\n", + " a.add_feature(cfeature.COASTLINE)" + ] + }, + { + "cell_type": "raw", + "id": "55cf7674-4113-4da9-b288-beb5f5569dc1", + "metadata": {}, + "source": [ + "# Can't seem to use uxarray for lat-lon data?\n", + "dc = ux_fv[\"GPP\"].mean('time').to_polycollection(projection=projection, override=True)\n", + "dc.set_antialiased(False)\n", + "dc.set_transform(transform)\n", + "dc.set_antialiased(False)\n", + "dc.set_clim(vmin=0, vmax=1e-4)\n", + "\n", + "fig, ax = plt.subplots(\n", + " 1,\n", + " 1,\n", + " figsize=(5, 5),\n", + " facecolor=\"w\",\n", + " constrained_layout=True,\n", + " subplot_kw=dict(projection=projection),\n", + ")\n", + "\n", + "# add geographic features\n", + "ax.add_feature(cfeature.COASTLINE)\n", + "\n", + "ax.add_collection(dc)\n", + "ax.set_global()\n", + "cbar = plt.colorbar(dc, ax=ax, orientation='horizontal', pad=0.05, shrink=0.8)\n", + "cbar.set_label('Data Values')\n", + "\n", + "plt.title(\"ne30 w/ uxarray\") ;" + ] + }, + { + "cell_type": "markdown", + "id": "232aefe3-d7dc-4643-8155-7010009896db", + "metadata": {}, + "source": [ + "---------\n", + "### Subsetting data for Regional plots\n", + "Example at https://uxarray.readthedocs.io/en/latest/user-guide/subset.html\n", + "1. Look at test data, so see how coastlines are handled\n", + "2. Look at regional fluxes and compare raw and regridded data\n", + "3. Since climatologies weren't identical, tried weighting fluxes by source landfrac too, but these results don't look great." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a6287eac-a56f-4695-8f40-350b8ea779c3", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.4.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete 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console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) 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const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.holoviz.org/panel/1.4.4/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.12.0/dist/geoviews.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw 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loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " 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" }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " 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'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of 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else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
    \n", + "
    \n", + "
    \n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1009" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
    \n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
    \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import holoviews as hv\n", + "plot_opts = {\"width\": 700, \"height\": 350}\n", + "hv.extension(\"bokeh\")\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "bccb5c83-1bbe-4e57-9a71-a9a9e0c735ad", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.0, 0.0001671932)\n" + ] + } + ], + "source": [ + "plot_opts = {\"width\": 700, \"height\": 400}\n", + "clim = (np.nanmin(ds0[\"GPP\"].values), np.nanmax(ds0[\"GPP\"].values))\n", + "print(clim)\n", + "features = gf.coastline(\n", + " projection=ccrs.PlateCarree(), line_width=1, scale=\"110m\"\n", + ") #* gf.states(projection=ccrs.PlateCarree(), line_width=1, scale=\"110m\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "1563ce17-1d11-47bf-9ee1-56f3612b6dfd", + "metadata": {}, + "outputs": [], + "source": [ + "# This takes a long time to plot, we'll skip it for now\n", + "#ds0[\"test\"][0].plot.polygons(\n", + "# title=\"Global Grid\", **plot_opts\n", + "#) * features" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c1a7ce6e-cdd0-4e3d-b0e9-41777d834241", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.4.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.holoviz.org/panel/1.4.4/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.12.0/dist/geoviews.min.js\"];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + "\ttry {\n", + " inline_js[i].call(root, root.Bokeh);\n", + "\t} catch(e) {\n", + "\t if (!reloading) {\n", + "\t throw e;\n", + "\t }\n", + "\t}\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.2.min.js\", \"https://cdn.holoviz.org/panel/1.4.4/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.12.0/dist/geoviews.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw 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    \n", + "
    \n", + "" + ], + "text/plain": [ + ":Overlay\n", + " .Polygons.I :Polygons [x,y] (test)\n", + " .Coastline.I :Feature [Longitude,Latitude]" + ] + }, + "execution_count": 22, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1013" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# set the bounding box\n", + "lon_bounds = (105, 145)\n", + "lat_bounds = (25, 58)\n", + "# elements include nodes, edge centers, or face centers) \n", + "element = 'face centers'\n", + "\n", + "bbox_subset_nodes = ds0[\"test\"][5].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element\n", + ")\n", + "bbox_subset_nodes.plot.polygons(\n", + " cmap='viridis',\n", + " title=\"Bounding Box Subset (\"+element+\")\",\n", + " **plot_opts,\n", + ") * features" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "87fb2283-e414-4c5b-99a0-d337f2f40b99", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(12,8))\n", + "axs=axs.flatten()\n", + "# These differences around the coast seem pretty tiny, again within rounding error?\n", + "ds_out_con.test.isel(time=0)\\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[0])\n", + "\n", + "ds_out_bilin.test.isel(time=0)\\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[1]) ;\n", + "\n", + "ds_out_con.test.isel(time=0).where(fv_t232.landfrac>0)\\\n", + " .sel(lon=slice(lon_bounds[0],lon_bounds[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=1-1e-8, vmax=1+1e-8, ax=axs[2])\n", + "\n", + "axs[0].set_title('conservative test, no mask')\n", + "axs[1].set_title('bilinear test') ;\n", + "axs[2].set_title('conservative test, destination mask') ;" + ] + }, + { + "cell_type": "markdown", + "id": "7dd34b5d-bd99-405a-a988-5b561bf3d0b0", + "metadata": {}, + "source": [ + "#### Now look at regional fluxes\n", + "- Not sure if bounding boxes are necessarily identical in unstructured and regular grid.\n", + "- Fluxes still don't look the same when focusing on a few islands, but overall not unreasonable\n", + "- What level of difference are we OK tolerating?" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d94d7a9a-994a-4c0a-ab57-db7bebcc479f", + "metadata": {}, + "outputs": [], + "source": [ + "# elements include nodes, edge centers, or face centers) \n", + "element = 'face centers'\n", + "region = 'Hawaii'\n", + "month = 6\n", + "# set the bounding box\n", + "plot_opts = {\"width\": 700, \"height\": 400}\n", + "\n", + "if region == 'Global':\n", + " lat_bounds = (-90, 90)\n", + " lon_bounds = (-180, 180)\n", + " lon_bounds2 = (0, 360)\n", + "elif region == 'East Asia':\n", + " lat_bounds = (23, 58)\n", + " lon_bounds = (110, 150)\n", + " lon_bounds2 = (110, 150)\n", + "elif region == 'Polar':\n", + " lat_bounds = (60, 90)\n", + " lon_bounds = (-180, 180)\n", + " lon_bounds2 = (0, 360)\n", + "elif region == 'Hawaii':\n", + " lat_bounds = (17, 25)\n", + " lon_bounds = (-162, -153)\n", + " lon_bounds2 = ((360-162), (360-153)) \n", + "elif region == 'Amazon':\n", + " lat_bounds = (-10, 0)\n", + " lon_bounds = (-70, -50)\n", + " lon_bounds2 = ((290), (310)) \n", + "elif region == 'New Zeland':\n", + " lat_bounds = (-50, -33)\n", + " lon_bounds = (160, 179)\n", + " lon_bounds2 = (160, 180)\n", + "elif region == 'South America':\n", + " lat_bounds = (-57, 13)\n", + " lon_bounds = (-85, -30)\n", + " lon_bounds2 = ((360-85), (360-30))\n", + " plot_opts = {\"width\": 700, \"height\": 700} \n", + "\n", + "\n", + "bbox_subset_nodes = ds0[\"GPP\"][month].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "e3c6a535-a39a-4c84-8044-3184d5e94a2d", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
    \n", + "
    \n", + "
    \n", + "" + ], + "text/plain": [ + ":Overlay\n", + " .Polygons.I :Polygons [x,y] (GPP)\n", + " .Coastline.I :Feature [Longitude,Latitude]" + ] + }, + "execution_count": 25, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p3823" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "#if region != \"New Zeland\" comment out features below \n", + "bbox_subset_nodes.plot.polygons(\n", + " clim=clim, \n", + " cmap='viridis',\n", + " title=region + \" Bounding Box Subset (\"+element+\" Query)\",\n", + " **plot_opts,\n", + ") * features" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d7fbcc60-32a2-4fef-afad-06e520c47635", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if region == \"New Zeland\":\n", + " fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " figsize=(12,8))\n", + "else:\n", + " fig, axs = plt.subplots(nrows=2,ncols=2,\n", + " subplot_kw=dict(projection=ccrs.PlateCarree()),\n", + " figsize=(12,8))\n", + "axs=axs.flatten()\n", + "ds_out_con.GPP.isel(time=month)\\\n", + " .sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=clim[0],vmax=clim[1], ax=axs[0]) \n", + "axs[0].set_title(region + ' conservaitve, no mask')\n", + "\n", + "ds_out_con.GPP.isel(time=month).where(fv_t232.landfrac>0)\\\n", + " .sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=clim[0],vmax=clim[1], ax=axs[1]) \n", + "axs[1].set_title(region + ' conservaitve, destination mask')\n", + "\n", + "\n", + "ds_out_bilin.GPP.isel(time=month)\\\n", + " .sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1]))\\\n", + " .plot(vmin=clim[0],vmax=clim[1], ax=axs[2]) \n", + "axs[2].set_title(region + ' bilinear') ;\n", + "\n", + "if region != \"New Zeland\":\n", + " for a in axs:\n", + " a.coastlines()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4659783e-c119-4031-9988-61e43f659583", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "59.7\n", + "721.1\n", + "716.506\n", + "717.332\n", + "701.8\n" + ] + } + ], + "source": [ + "# elements include nodes, edge centers, or face centers) \n", + "element = 'face centers'\n", + "element = 'nodes'\n", + "\n", + "var = 'GPP'\n", + "bbox_var = ds0[var].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element)\n", + "\n", + "bbox_area = ds0[\"area\"].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element)\n", + "\n", + "bbox_landfrac = ds0[\"landfrac\"].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=element)\n", + "\n", + "# Area weighting\n", + "bbox_wgt = bbox_area * bbox_landfrac / ((bbox_area * bbox_landfrac).sum())\n", + "y = (bbox_var * bbox_wgt).sum('n_face').values\n", + "x = bbox_var['time'].values\n", + "\n", + "plt.plot(x, y, label = 'raw ne30, ' + str(np.round(y.mean() * spy, 1))) \n", + "\n", + "#repeat for regridded climo\n", + "bbox_area_r = fv_t232['area'].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_landfrac_r = fv_t232['landfrac'].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_wgt_r = bbox_area_r * bbox_landfrac_r / ((bbox_area_r * bbox_landfrac_r).sum())\n", + "\n", + "# Better with destination area\n", + "bbox_area_rB = ds_out_con['area'].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_landfrac_rB = ds_out_con['landfrac'].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_landfrac_rC = ds_out_con['landfrac'].where(fv_t232['landmask']==1).sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "bbox_wgt_rB = bbox_area_r * bbox_landfrac_rB / ((bbox_area_r * bbox_landfrac_rB).sum())\n", + "bbox_wgt_rC = bbox_area_r * bbox_landfrac_rC / ((bbox_area_r * bbox_landfrac_rC).sum())\n", + "\n", + "bbox_var_r = ds_out_con[var].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "y_r = (bbox_var_r * bbox_wgt_r).sum(['lat','lon']).values\n", + "y_rB = (bbox_var_r * bbox_wgt_rB).sum(['lat','lon']).values\n", + "y_rC = (bbox_var_r * bbox_wgt_rC).sum(['lat','lon']).values\n", + "plt.plot(x, y_r, label = 'cons destination mask & landfrac, ' + str(np.round(y_r.mean()* spy,1))) \n", + "plt.plot(x, y_rB, label = 'cons. no mask regridded landfrac ' + str(np.round(y_rB.mean()* spy,1))) \n", + "\n", + "bbox_var_r2 = ds_out_bilin[var].sel(lon=slice(lon_bounds2[0],lon_bounds2[1]),lat=slice(lat_bounds[0],lat_bounds[1])) \n", + "y_r2 = (bbox_var_r2 * bbox_wgt_r).sum(['lat','lon']).values\n", + "plt.plot(x, y_r2,\n", + " label= 'bilinear destination mask & landfrac, ' + str(np.round(y_r2.mean()* spy,1))) \n", + "\n", + "plt.title(region + ' climatology, annual regional integral (gC/y)')\n", + "plt.legend()\n", + "plt.show();\n", + "# Print mean annual flux from region (not time weighted correctly)\n", + "print(np.round(y.mean()* spy,1))\n", + "print(np.round(y_r.mean()* spy,1))\n", + "print(np.round(y_rB.mean()* spy,3))\n", + "print(np.round(y_rC.mean()* spy,3))\n", + "print(np.round(y_r2.mean()* spy,1))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "537d79e9-58a4-4c50-a251-a15e1ab56852", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 4B\n", + "array(1.7618376, dtype=float32)\n" + ] + }, + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " Size: 4B\n", + "array(1.762814, dtype=float32)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(bbox_landfrac_r.sum()) #destination land frac sum\n", + "\n", + "bbox_landfrac_rB.sum() #regridded land frac sum\n", + "#bbox_wgt_rC.plot(vmax=0.18,vmin=0.04)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "293363a0-ffd0-4a02-a503-ba1681e3eb20", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "(bbox_landfrac_rC*bbox_var_r).sum(['lat','lon']).plot(label='dest mask')\n", + "(bbox_landfrac_rB*bbox_var_r).sum(['lat','lon']).plot(label='no mask')\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "5fa74a76-d239-4155-a123-3deadd3cbec5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24003.926\n", + "288054.2\n" + ] + } + ], + "source": [ + " print((bbox_area_r * bbox_landfrac_r).sum().values)\n", + " print((bbox_area * bbox_landfrac).sum().values)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "db8184cd-e3a6-4585-9eec-ead1704ec0f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/glade/campaign/cesm/cesmdata/inputdata/share/meshes/ne30pg3_ESMFmesh_cdf5_c20211018.nc'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mesh0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a8b6931-dfd1-4c10-aace-fa24f93edf4f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "NPL 2024b", + "language": "python", + "name": "npl-2024b" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/regridding/regrid_se_to_fv.py b/scripts/regridding/regrid_se_to_fv.py new file mode 100644 index 000000000..617726f62 --- /dev/null +++ b/scripts/regridding/regrid_se_to_fv.py @@ -0,0 +1,106 @@ +# Regrids unstructured SE grid to regular lat-lon +# Shamelessly borrowed from @maritsandstad with NorESM who deserves credit for this work +# https://github.com/NorESMhub/xesmf_clm_fates_diagnostic/blob/main/src/xesmf_clm_fates_diagnostic/plotting_methods.py + +import xarray as xr +import xesmf +import numpy as np + +def make_se_regridder(weight_file, s_data, d_data, + Method='coservative' + ): + weights = xr.open_dataset(weight_file) + in_shape = weights.src_grid_dims.load().data + + # Since xESMF expects 2D vars, we'll insert a dummy dimension of size-1 + if len(in_shape) == 1: + in_shape = [1, in_shape.item()] + + # output variable shape + out_shape = weights.dst_grid_dims.load().data.tolist()[::-1] + + dummy_in = xr.Dataset( + { + "lat": ("lat", np.empty((in_shape[0],))), + "lon": ("lon", np.empty((in_shape[1],))), + } + ) + dummy_out = xr.Dataset( + { + "lat": ("lat", weights.yc_b.data.reshape(out_shape)[:, 0]), + "lon": ("lon", weights.xc_b.data.reshape(out_shape)[0, :]), + } + ) + # Hard code masks for now, not sure this does anything? + if isinstance(s_data, xr.DataArray): + s_mask = xr.DataArray(s_data.data.reshape(in_shape[0],in_shape[1]), dims=("lat", "lon")) + dummy_in['mask']= s_mask + if isinstance(d_data, xr.DataArray): + d_mask = xr.DataArray(d_data.values, dims=("lat", "lon")) + dummy_out['mask']= d_mask + + # do source and destination grids need masks here? + # See xesmf docs https://xesmf.readthedocs.io/en/stable/notebooks/Masking.html#Regridding-with-a-mask + regridder = xesmf.Regridder( + dummy_in, + dummy_out, + weights=weight_file, + # results seem insensitive to this method choice + # choices are coservative_normed, coservative, and bilinear + method=Method, + reuse_weights=True, + periodic=True, + ) + return regridder + +def regrid_se_data_bilinear(regridder, data_to_regrid, comp_grid): + updated = data_to_regrid.copy().transpose(..., comp_grid).expand_dims("dummy", axis=-2) + regridded = regridder(updated.rename({"dummy": "lat", comp_grid: "lon"}), + skipna=True, na_thres=1, + ) + return regridded + +def regrid_se_data_conservative(regridder, data_to_regrid, comp_grid="lndgrid"): + updated = data_to_regrid.copy().transpose(..., comp_grid).expand_dims("dummy", axis=-2) + regridded = regridder(updated.rename({"dummy": "lat", comp_grid: "lon"}) ) + return regridded + + +def regrid_atm_se_data_bilinear(regridder, data_to_regrid, comp_grid='ncol'): + if isinstance(data_to_regrid, xr.Dataset): + vars_with_ncol = [name for name in data_to_regrid.variables if comp_grid in data_to_regrid[name].dims] + updated = data_to_regrid.copy().update(data_to_regrid[vars_with_ncol].transpose(..., comp_grid).expand_dims("dummy", axis=-2)) + elif isinstance(data_to_regrid, xr.DataArray): + updated = data_to_regrid.transpose(...,comp_grid).expand_dims("dummy",axis=-2) + else: + raise ValueError(f"Something is wrong because the data to regrid isn't xarray: {type(data_to_regrid)}") + regridded = regridder(updated) + return regridded + + +def regrid_atm_se_data_conservative(regridder, data_to_regrid, comp_grid='ncol'): + if isinstance(data_to_regrid, xr.Dataset): + vars_with_ncol = [name for name in data_to_regrid.variables if comp_grid in data_to_regrid[name].dims] + updated = data_to_regrid.copy().update(data_to_regrid[vars_with_ncol].transpose(..., comp_grid).expand_dims("dummy", axis=-2)) + elif isinstance(data_to_regrid, xr.DataArray): + updated = data_to_regrid.transpose(...,comp_grid).expand_dims("dummy",axis=-2) + else: + raise ValueError(f"Something is wrong because the data to regrid isn't xarray: {type(data_to_regrid)}") + regridded = regridder(updated,skipna=True, na_thres=1) + return regridded + + + +""" +def regrid_lnd_se_data_bilinear(regridder, data_to_regrid, comp_grid): + updated = data_to_regrid.copy().transpose(..., comp_grid).expand_dims("dummy", axis=-2) + regridded = regridder(updated.rename({"dummy": "lat", comp_grid: "lon"}), + skipna=True, na_thres=1, + ) + return regridded + + +def regrid_lnd_se_data_conservative(regridder, data_to_regrid, comp_grid): + updated = data_to_regrid.copy().transpose(..., comp_grid).expand_dims("dummy", axis=-2) + regridded = regridder(updated.rename({"dummy": "lat", comp_grid: "lon"}) ) + return regridded"""