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Revision 8b4e4eb9

Added by Benoit Parmentier over 8 years ago

testing alternative metrics for accuracy layers: mae and n

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climate/research/oregon/interpolation/master_script_stage_7.R
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#AUTHOR: Benoit Parmentier                                                                        


  		




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#CREATED ON: 01/01/2016  


  		




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#MODIFIED ON: 05/31/2016  


  		




  
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#MODIFIED ON: 06/07/2016  


  		




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#PROJECT: NCEAS INPLANT: Environment and Organisms                                                                           


  		




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#First source these files:


  		




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num_cores <- 6 #PARAM 8, arg 8         


  		




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max_mem = 1e+07 #param 9, arg 9


  		




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mosaicing_method <- "use_edge_weights" #PARAM10, arg 10


  		




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metric_name <- "rmse" #RMSE, MAE etc. #PARAM 11, arg 11


  		




  
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metric_name <- "rmse" # "mae", "r" for MAE, R etc.; can also be ns or nv? #PARAM 11, arg 11


  		




  
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#metric_name <- "n"


  		




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#day_start <- "19990701" #PARAM 12 arg 12


  		




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#day_end <- "19990703" #PARAM 13 arg 13


  		




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day_start <- "19990108" #PARAM 12 arg 12


  		




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algorithm <- "python" #PARAM 16 #if R use mosaic function for R, if python use modified gdalmerge script from Alberto Guzmann


  		




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#layers_option <- c("var_pred") #arg 17 ,param 17, options are:#res_training, res_testing,ac_training, ac_testing, var_pred


  		




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layers_option <- c("ac_testing") #arg 17 ,param 17, options are:#res_training, res_testing,ac_training, ac_testing, var_pred


  		




  
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#layers_option <- c("res_testing") #arg 17 ,param 17, options are:#res_training, res_testing,ac_training, ac_testing, var_pred


  		




  
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layers_option <- c("res_training") #arg 17 ,param 17, options are:#res_training, res_testing,ac_training, ac_testing, var_pred


  		




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tmp_files <- TRUE #arg 18, param 18, keep temp files if TRUE


  		




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data_type <- "Int16" #, param 19, use int32 for output layers mosaiced


  		




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#debug_fun_test(list_param_raster_prediction)


  		




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i <- 1 #this select the first year of list_year_predicted


  		




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#function_mosaicing_functions <- "global_run_scalingup_mosaicing_function_05312016.R" #PARAM12


  		




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#function_mosaicing <-"global_run_scalingup_mosaicing_05312016.R"


  		




  
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#function_mosaicing <-"global_run_scalingup_mosaicing_06072016.R"


  		




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#source(file.path(script_path,function_mosaicing)) #source all functions used in this script 


  		




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#source(file.path(script_path,function_mosaicing_functions)) #source all functions used in this script 


  		




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