diff --git a/README.md b/README.md index 3ee9e0d4..ded1d431 100644 --- a/README.md +++ b/README.md @@ -1,34 +1,59 @@ SSMSE: Management Strategy Evaluation for Stock Synthesis ================ - - [SSMSE build status](#ssmse-build-status) - - [Motivation for developing SSMSE](#motivation-for-developing-ssmse) - - [Need Help?](#need-help) - - [Installing the SSMSE R package](#installing-the-ssmse-r-package) - - [Troubleshooting Installation](#troubleshooting-installation) - - [An SSMSE example](#an-ssmse-example) - - [Setup R workspace folders](#setup-r-workspace-folders) - - [Create the operating models - (OMs)](#create-the-operating-models-oms) - - [Adding process error through recruitment deviations and - time-varying - selectivity](#adding-process-error-through-recruitment-deviations-and-time-varying-selectivity) - - [Examine the management procedure - used](#examine-the-management-procedure-used) - - [Run SSMSE](#run-ssmse) - - [run\_SSMSE output](#run_ssmse-output) - - [Performance metrics](#performance-metrics) - - [Summarize results](#summarize-results) - - [Simple Convergence Check](#simple-convergence-check) - - [Plot Spawning Stock Biomass - (SSB)](#plot-spawning-stock-biomass-ssb) - - [Example MSE Results](#example-mse-results) - - [Delete the files](#delete-the-files) - - [Advanced options: use a custom management - strategy/procedure](#advanced-options-use-a-custom-management-strategyprocedure) - - [How can I contribute to SSMSE?](#how-can-i-contribute-to-ssmse) - - [Roadmap: Where is SSMSE headed - next?](#roadmap-where-is-ssmse-headed-next) +- SSMSE build + status +- Motivation for developing + SSMSE +- How to cite + SSMSE +- Need Help? +- Installing the SSMSE R + package +- Troubleshooting Installation +- An SSMSE + example + - Setup R workspace folders + - Create the operating models + (OMs) + - Adding + process error through recruitment deviations and time-varying + selectivity + - Examine the management + procedure used + - Run SSMSE + - run_SSMSE + output + - Performance + metrics + - Summarize + results + - Simple Convergence Check + - Plot Spawning Stock Biomass + (SSB) + - Example MSE + Results + - Delete the + files +- Advanced + options: use a custom management strategy/procedure +- How can I contribute to + SSMSE? +- Roadmap: Where is SSMSE + headed next? @@ -36,18 +61,19 @@ SSMSE: Management Strategy Evaluation for Stock Synthesis # SSMSE build status -[![R-CMD-check](https://github.com/r4ss/r4ss/workflows/R-CMD-check/badge.svg)](https://github.com/nmfs-fish-tools/SSMSE/actions?query=workflow%3AR-CMD-check) +[![call-r-cmd-check](https://github.com/nmfs-fish-tools/SSMSE/actions/workflows/call-r-cmd-check.yml/badge.svg)](https://github.com/nmfs-fish-tools/SSMSE/actions/workflows/call-r-cmd-check.yml) [![codecov](https://codecov.io/gh/nmfs-fish-tools/SSMSE/branch/master/graph/badge.svg)](https://codecov.io/gh/nmfs-fish-tools/SSMSE) +[![DOI](https://joss.theoj.org/papers/10.21105/joss.04937/status.svg)](https://doi.org/10.21105/joss.04937) ------ +------------------------------------------------------------------------ ------ +------------------------------------------------------------------------ **This is a repository for the Stock Assessment Tool: SSMSE** - - Supported by the NOAA Fisheries Integrated Toolbox +- Supported by the NOAA Fisheries Integrated Toolbox **Disclaimer** @@ -65,9 +91,7 @@ The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.” - - ------ +------------------------------------------------------------------------ # Motivation for developing SSMSE @@ -78,16 +102,23 @@ conditioned Stock Synthesis mode to use as the OM. The intended users are those who conduct MSEs. Below, we’ll work through a simple example MSE as a way of introducing -the SSMSE package. +the SSMSE package. An additional example is included in the [Journal Of +Open Source Software SSMSE paper](https://doi.org/10.21105/joss.04937). + +# How to cite SSMSE + +Doering et al., (2023). SSMSE: An R package for Management Strategy +Evaluation with Stock Synthesis Operating Models. Journal of Open Source +Software, 8(90), 4937, # Need Help? - - Get questions answered in - [discussions](https://github.com/nmfs-fish-tools/SSMSE/discussions). - - Submit bug reports and feature requests to - [issues](https://github.com/nmfs-fish-tools/SSMSE/issues). - - Alternatively, contact the develpers via email at - . +- Get questions answered in + [discussions](https://github.com/nmfs-fish-tools/SSMSE/discussions). +- Submit bug reports and feature requests to + [issues](https://github.com/nmfs-fish-tools/SSMSE/issues). +- Alternatively, contact the develpers via email at + . # Installing the SSMSE R package @@ -103,31 +134,39 @@ You can read the help files with ?SSMSE ``` +Note that when SSMSE is installed, [Stock +Synthesis](https://github.com/nmfs-stock-synthesis/stock-synthesis) +binaries are included in the `SSMSE/bin/` folder downloaded to the +user’s local R library. Currently, the Stock Synthesis 3.30.18 is +included. Separate binaries are included for Windows, Mac and Linux. +Users may swap out these binaries, but SSMSE is not tested with other +versions of SS3. + # Troubleshooting Installation Here are some tips: - - Make sure you are using the main branch versions of R packages - `r4ss` and `ss3sim`. These can be installed separately using - `remotes::install_github("r4ss/r4ss")` and - `remotes::install_github("ss3sim/ss3sim")`. - - If R asks “Would you like to download from sources that need - compilation?”, select “no”, as the older compiled versions should - work fine. - - If R asks which packages you would like to update, select “none.” - Although it is good to update to keep packages current, install can - sometimes be frustrating when updating many packages and skipping - this step can eliminate issues in the short term. - - If running into an error during install, try closing out all R - sessions open (e.g., other R GUI or R studio windows), restarting - the R session and trying - `remotes::install_github("nmfs-fish-tools/SSMSE")` again. - - If still running into an error during install, and it seems to be a - problem installing a particular package, try restarting the R - session, using `install.packages()` to download the package that - caused the error, restarting the R session, and trying - `remotes::install_github("nmfs-fish-tools/SSMSE")` again. This step - may need to be done several times for different R packages. +- Make sure you are using the main branch versions of R packages `r4ss` + and `ss3sim`. These can be installed separately using + `remotes::install_github("r4ss/r4ss")` and + `remotes::install_github("ss3sim/ss3sim")`. +- If R asks “Would you like to download from sources that need + compilation?”, select “no”, as the older compiled versions should work + fine. +- If R asks which packages you would like to update, select “none.” + Although it is good to update to keep packages current, install can + sometimes be frustrating when updating many packages and skipping this + step can eliminate issues in the short term. +- If running into an error during install, try closing out all R + sessions open (e.g., other R GUI or R studio windows), restarting the + R session and trying + `remotes::install_github("nmfs-fish-tools/SSMSE")` again. +- If still running into an error during install, and it seems to be a + problem installing a particular package, try restarting the R session, + using `install.packages()` to download the package that caused the + error, restarting the R session, and trying + `remotes::install_github("nmfs-fish-tools/SSMSE")` again. This step + may need to be done several times for different R packages. Still having trouble installing SSMSE? Please don’t hesitate to [post in discussions about the @@ -164,8 +203,9 @@ First, we will load the `SSMSE` package and create a folder in which to run the example: ``` r -library(SSMSE) #load the package -library(r4ss) #install using remotes::install_github("r4ss/r4ss) +library(SSMSE) # load the package +## Welcome to SSMSE version 0.2.8 using Stock Synthesis 3.30.18. +library(r4ss) # install using remotes::install_github("r4ss/r4ss) ## ## Attaching package: 'r4ss' ## The following object is masked from 'package:stats': @@ -191,9 +231,9 @@ dir.create(run_SSMSE_dir) The cod model with h = 0.65 (as in scenario 1) is included as external package data in SSMSE. However, we will need to modify it to use as an operating model with h = 1 (as in scenario 2). Note in this case that -refit\_OM is false, so the model is not being refit, just run through +refit_OM is false, so the model is not being refit, just run through without fitting. To condition the new model on the same data as the -input model, refit\_OM should be TRUE. +input model, refit_OM should be TRUE. First, we identify where the base cod model is stored, modify it such that the steepness parameter is 1, and save the modified cod OM for @@ -203,8 +243,10 @@ scenario 2 in a new folder in the `run_SSMSE_dir` directory. cod_mod_path <- system.file("extdata", "models", "cod", package = "SSMSE") # develop_OMs will save a model called "cod_SR_BH_steep_1" in the out_dir # specified -develop_OMs(OM_name = "cod", out_dir = run_SSMSE_dir, par_name = "SR_BH_steep", - par_vals = 1, refit_OMs = FALSE, hess = FALSE) +develop_OMs( + OM_name = "cod", out_dir = run_SSMSE_dir, par_name = "SR_BH_steep", + par_vals = 1, refit_OMs = FALSE, hess = FALSE +) # OM model for scenario 2 cod_1_path <- file.path(run_SSMSE_dir, "cod_SR_BH_steep_1") ``` @@ -224,7 +266,7 @@ assumed default). ``` r # Start from a list created by a helper function -template_mod_change <- create_future_om_list() +template_mod_change <- create_future_om_list() # add recruitment deviations rec_dev_specify <- template_mod_change[[1]] rec_dev_specify$pars <- "rec_devs" # apply change to rec devs @@ -296,8 +338,10 @@ Finally, add these two changes together into an object to pass to `run_SSMSE` ``` r -future_om_list_recdevs_sel <- list(rec_dev_specify, - mod_change_sel) +future_om_list_recdevs_sel <- list( + rec_dev_specify, + mod_change_sel +) ``` ### Add observation error through sampling from OM @@ -390,8 +434,9 @@ it is likely that multiple management procedures would be compared. ``` r fore <- r4ss::SS_readforecast( system.file("extdata", "models", "cod", "forecast.ss", package = "SSMSE"), - verbose = FALSE) -fore$Forecast + verbose = FALSE +) +fore$Forecast ## [1] 3 fore$Btarget ## [1] 0.4 @@ -435,7 +480,8 @@ More information on using the forecast module in SS3 to forecast catches is available in the [Stock Synthesis users manual](https://vlab.noaa.gov/web/stock-synthesis/document-library/-/document_library/0LmuycloZeIt/view/4513132). -Users can also specify their own \[custom management procedures\] +Users can also specify their own [custom management +procedures](https://nmfs-fish-tools.github.io/SSMSE/manual/custom-MS.html). ## Run SSMSE @@ -447,21 +493,22 @@ min): run_res_path <- file.path(run_SSMSE_dir, "results") dir.create(run_res_path) res <- run_SSMSE( - scen_name_vec = c("h-ctl", "h-1"),# name of the scenario - out_dir_scen_vec = run_res_path, # directory in which to run the scenario - iter_vec = c(5,5), # run with 5 iterations each - OM_name_vec = NULL, # specify directories instead - OM_in_dir_vec = c(cod_mod_path, normalizePath(cod_1_path)), # OM files - EM_name_vec = c("cod", "cod"), # cod is included in package data - MS_vec = c("EM","EM"), # The management strategy is specified in the EM - nyrs_vec = c(6, 6), # Years to project OM forward - nyrs_assess_vec = c(3, 3), # Years between assessments - future_om_list = future_om_list_recdevs_sel, - run_parallel = FALSE, # Run iterations in parallel - sample_struct_list = sample_struct_list_all, # How to sample data for running the EM. - sample_struct_hist_list = NULL, # because this is null, will just use sampling - # as in the current OM data file for the historical period. - seed = 12345) #Set a fixed integer seed that allows replication + scen_name_vec = c("h-ctl", "h-1"), # name of the scenario + out_dir_scen_vec = run_res_path, # directory in which to run the scenario + iter_vec = c(5, 5), # run with 5 iterations each + OM_name_vec = NULL, # specify directories instead + OM_in_dir_vec = c(cod_mod_path, normalizePath(cod_1_path)), # OM files + EM_name_vec = c("cod", "cod"), # cod is included in package data + MS_vec = c("EM", "EM"), # The management strategy is specified in the EM + nyrs_vec = c(6, 6), # Years to project OM forward + nyrs_assess_vec = c(3, 3), # Years between assessments + future_om_list = future_om_list_recdevs_sel, + run_parallel = FALSE, # Run iterations in parallel + sample_struct_list = sample_struct_list_all, # How to sample data for running the EM. + sample_struct_hist_list = NULL, # because this is null, will just use sampling + # as in the current OM data file for the historical period. + seed = 12345 +) # Set a fixed integer seed that allows replication ``` See `?run_SSMSE` for more details on function arguments. In a real MSE @@ -470,13 +517,13 @@ uncertainty (given observation and process errors) in the results would be preferred. However, we are only running 5 iterations per scenario in this demonstration to reduce computing time. -## run\_SSMSE output +## run_SSMSE output `run_SSMSE` will create new folders in the folders specified in `out_dir_scen_vec` (note that in this case, we are running both scenarios in the same folder). After is complete, there will be a folder -for each scenario in `run_res_path` (since `out_dir_scen_vec = -run_res_path` in this example): +for each scenario in `run_res_path` (since +`out_dir_scen_vec = run_res_path` in this example): ``` r list.dirs(run_res_path, recursive = FALSE) @@ -537,17 +584,15 @@ the results are stored. summary <- SSMSE_summary_all(run_res_path) ## Extracting results from 2 scenarios ## Starting h-1 with 5 iterations -## Warning: The `ncols` argument of `SS_output()` is deprecated as of r4ss 1.46.0. -## Input 'ncols' no longer needed. ## Starting h-ctl with 5 iterations ``` Plotting and data manipulation can then be done with these summaries. For example, SSB over time by model can be plotted. The models include -the Operating Model (cod\_OM), Estimation model (EM) for the historical -period of years 0-100 (cod\_EM\_init), and the EM run with last year of -data in year 103 (cod\_EM\_103). The operating models are shown in blue -or black (depending on the scenario), and the estimation model runs are +the Operating Model (cod_OM), Estimation model (EM) for the historical +period of years 0-100 (cod_EM_init), and the EM run with last year of +data in year 103 (cod_EM_103). The operating models are shown in blue or +black (depending on the scenario), and the estimation model runs are shown in orange, and the scenarios are shown on different subplots: ``` r @@ -572,28 +617,28 @@ too large ``` r check_convergence <- function(summary, min_yr = 101, max_yr = 120, n_EMs = 5) { require(dplyr) # note: not the best way to do this - if(any(!is.na(summary$scalar$params_on_bound))) { + if (any(!is.na(summary$scalar$params_on_bound))) { warning("Params on bounds") } else { message("No params on bounds") } summary$ts$model_type <- ifelse(grepl("_EM_", summary$ts$model_run), "EM", "OM") calc_SSB <- summary$ts %>% - filter(year >= min_yr & year <= max_yr) %>% + filter(year >= min_yr & year <= max_yr) %>% select(iteration, scenario, year, model_run, model_type, SpawnBio) - OM_vals <- calc_SSB %>% - filter(model_type == "OM") %>% - rename(SpawnBio_OM = SpawnBio ) %>% - select(iteration, scenario, year, SpawnBio_OM) - EM_vals <- calc_SSB %>% - filter(model_type == "EM") %>% - rename(SpawnBio_EM = SpawnBio) %>% - select(iteration, scenario, year, model_run, SpawnBio_EM) - bind_vals <- full_join(EM_vals, OM_vals, by = c("iteration", "scenario", "year")) %>% - mutate(SSB_ratio = SpawnBio_EM/SpawnBio_OM) - filter_SSB <- bind_vals %>% + OM_vals <- calc_SSB %>% + filter(model_type == "OM") %>% + rename(SpawnBio_OM = SpawnBio) %>% + select(iteration, scenario, year, SpawnBio_OM) + EM_vals <- calc_SSB %>% + filter(model_type == "EM") %>% + rename(SpawnBio_EM = SpawnBio) %>% + select(iteration, scenario, year, model_run, SpawnBio_EM) + bind_vals <- full_join(EM_vals, OM_vals, by = c("iteration", "scenario", "year")) %>% + mutate(SSB_ratio = SpawnBio_EM / SpawnBio_OM) + filter_SSB <- bind_vals %>% filter(SSB_ratio > 2 | SSB_ratio < 0.5) - if(nrow(filter_SSB) > 0 ) { + if (nrow(filter_SSB) > 0) { warning("Some large/small SSBs relative to OM") } else { message("All SSBs in EM are no greater than double and no less than half SSB vals in the OM") @@ -613,17 +658,22 @@ interest. ``` r # plot SSB by year and model run -ggplot2::ggplot(data = subset(summary$ts, model_run %in% c("cod_OM", "cod_SR_BH_steep_1_OM", "cod_EM_103")), - ggplot2::aes(x = year, y = SpawnBio)) + +ggplot2::ggplot( + data = subset(summary$ts, model_run %in% c("cod_OM", "cod_SR_BH_steep_1_OM", "cod_EM_103")), + ggplot2::aes(x = year, y = SpawnBio) +) + ggplot2::geom_vline(xintercept = 100, color = "gray") + - ggplot2::geom_line(ggplot2::aes(linetype = as.character(iteration), color = model_run))+ + ggplot2::geom_line(ggplot2::aes(linetype = as.character(iteration), color = model_run)) + ggplot2::scale_color_manual(values = c("#D65F00", "black", "blue")) + ggplot2::scale_linetype_manual(values = rep("solid", 50)) + ggplot2::guides(linetype = FALSE) + ggplot2::facet_wrap(. ~ scenario) + ggplot2::theme_classic() -## Warning: `guides( = FALSE)` is deprecated. Please use `guides( = -## "none")` instead. +## Warning: The `` argument of `guides()` cannot be `FALSE`. Use "none" instead as +## of ggplot2 3.3.4. +## This warning is displayed once every 8 hours. +## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was +## generated. ``` ![](man/figures/README-plot_SSB-1.png) @@ -639,13 +689,13 @@ spawning stock biomass (SSB) from years 104 to 106. get_SSB_avg <- function(summary, min_yr, max_yr) { OM_vals <- unique(summary$ts$model_run) OM_vals <- grep("_OM$", OM_vals, value = TRUE) - SSB_yr <- summary$ts %>% - filter(year >= min_yr & year <= max_yr) %>% - filter(model_run %in% OM_vals) %>% - select(iteration, scenario, year, SpawnBio) %>% - group_by(iteration, scenario) %>% - summarize(avg_SSB = mean(SpawnBio), .groups = "keep") %>% - ungroup() + SSB_yr <- summary$ts %>% + filter(year >= min_yr & year <= max_yr) %>% + filter(model_run %in% OM_vals) %>% + select(iteration, scenario, year, SpawnBio) %>% + group_by(iteration, scenario) %>% + summarize(avg_SSB = mean(SpawnBio), .groups = "keep") %>% + ungroup() SSB_yr } avg_SSB <- get_SSB_avg(summary, min_yr = 104, max_yr = 106) @@ -659,10 +709,14 @@ data_summary <- function(x) { } # Now, plot the average relative spawning stock biomass for years 104 - 106 ggplot(data = avg_SSB, aes(x = scenario, y = avg_SSB)) + - stat_summary(fun.data = data_summary, - position = position_dodge(width = 0.9), color = "blue") + - labs(title = "Long-term average SSB\n(years 104-106)", - x = "Scenario", y = "SSB") + + stat_summary( + fun.data = data_summary, + position = position_dodge(width = 0.9), color = "blue" + ) + + labs( + title = "Long-term average SSB\n(years 104-106)", + x = "Scenario", y = "SSB" + ) + theme_classic() ``` @@ -695,51 +749,56 @@ simple function that just sets future catches as half the sampled catches in a specified year: ``` r -constant_catch_MS <- function(OM_dat, nyrs_assess, catch_yr = 100, +constant_catch_MS <- function(OM_dat, nyrs_assess, catch_yr = 100, frac_catch = 0.5, ...) { # need to include ... to allow function to work # set catch the same as the previous year (sampled catch). # catch is in the same units as the operating model, in this case it is in # biomass. catch <- data.frame( year = (OM_dat$endyr + 1):(OM_dat$endyr + nyrs_assess), # the years to project the model forward - seas = 1, # hard coded from looking at model - fleet = 1, # hard coded from looking at model - catch = OM_dat$catch[OM_dat$catch$year == catch_yr, "catch"]*frac_catch, - catch_se = 0.05) # hard coded from looking at model + seas = 1, # hard coded from looking at model + fleet = 1, # hard coded from looking at model + catch = OM_dat$catch[OM_dat$catch$year == catch_yr, "catch"] * frac_catch, + catch_se = 0.05 + ) # hard coded from looking at model catch_bio <- catch # catch in biomass. In this case, catch is in biomass for both. Could also be left as NULL catch_F <- NULL # catch in terms of F, can be left as NULL. discards <- NULL # discards can be left as NULL if there are no discards - catch_list <- list(catch = catch, - catch_bio = catch_bio, - catch_F = catch_F, - discards = discards) + catch_list <- list( + catch = catch, + catch_bio = catch_bio, + catch_F = catch_F, + discards = discards + ) } ``` The function should be created in a separate file. In this case, assume -this function is available in a file custom\_funs.R. +this function is available in a file custom_funs.R. This function can then be used in a call to `run_SSMSE`: ``` r -run_result_custom <- run_SSMSE(scen_name_vec = "constant-catch", # name of the scenario - out_dir_scen_vec = run_res_path, # directory in which to run the scenario - iter_vec = 1, - OM_name_vec = "cod", # specify directories instead - OM_in_dir_vec = NULL, - MS_vec = "constant_catch_MS", # use the custom function - custom_MS_source = "custom_funs.R", # File where the custom function is available. - nyrs_vec = 6, # Years to project OM forward - nyrs_assess_vec = 3, # Years between assessments - future_om_list = future_om_list_recdevs_sel, - sample_struct_list = list(sample_struct_list[[1]]), # How to sample data for running the MS. - seed = 12345) #Set a fixed integer seed that allows replication +run_result_custom <- run_SSMSE( + scen_name_vec = "constant-catch", # name of the scenario + out_dir_scen_vec = run_res_path, # directory in which to run the scenario + iter_vec = 1, + OM_name_vec = "cod", # specify directories instead + OM_in_dir_vec = NULL, + MS_vec = "constant_catch_MS", # use the custom function + custom_MS_source = "custom_funs.R", # File where the custom function is available. + nyrs_vec = 6, # Years to project OM forward + nyrs_assess_vec = 3, # Years between assessments + future_om_list = future_om_list_recdevs_sel, + sample_struct_list = list(sample_struct_list[[1]]), # How to sample data for running the MS. + seed = 12345 +) # Set a fixed integer seed that allows replication ``` # How can I contribute to SSMSE? Currently, feedback on how using SSMSE is working or not would be most -hopeful\! Please open an issue if a problem is found. The developers can +hopeful! Please open an issue if a problem is found. The developers can also be contacted through [github discussions](https://github.com/nmfs-fish-tools/SSMSE/discussions) or by emailing . diff --git a/inst/bookdown/data/SSMSE_scalar.csv b/inst/bookdown/data/SSMSE_scalar.csv index 1af36798..458d47a1 100644 --- a/inst/bookdown/data/SSMSE_scalar.csv +++ b/inst/bookdown/data/SSMSE_scalar.csv @@ -1,31 +1,31 @@ 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1,1703660000,1657120000,203988000,144797000,27170900,144797000,27170900,0.0845307,0.470843,0.668789,0.668789,73,"cod_EM_init",2,"h-1" -1,1786630000,1761930000,106518000,149021000,24883200,149021000,24883200,0.0830849,0.465824,0.00258816,0.00258816,74,"cod_EM_init",2,"h-1" +1,1786630000,1761930000,106518000,149021000,24883200,149021000,24883200,0.0830849,0.465824,0.00258818,0.00258818,74,"cod_EM_init",2,"h-1" 1,1881110000,1843410000,58046000,154952000,26683100,154952000,26683100,0.0818305,0.461413,-0.616145,-0.616145,75,"cod_EM_init",2,"h-1" 1,1963430000,1941670000,96995300,157732000,24913800,157732000,24913800,0.0798738,0.454425,-0.115635,-0.115635,76,"cod_EM_init",2,"h-1" 1,2004570000,1988120000,138474000,155588000,21585000,155588000,21585000,0.0781412,0.448126,0.23467,0.23467,77,"cod_EM_init",2,"h-1" @@ -515,7 +515,7 @@ 1,3974250000,3945870000,101864000,0,0,0,0,0,0,-0.24785,-0.24785,9,"cod_EM_init",2,"h-1" 1,2168560000,2143200000,109648000,150269000,24837900,150269000,24837900,0.0682424,0.41,-0.0162158,-0.0162158,90,"cod_EM_init",2,"h-1" 1,2262420000,2242630000,149016000,151592000,21885200,151592000,21885200,0.0667744,0.404019,0.280486,0.280486,91,"cod_EM_init",2,"h-1" -1,2314010000,2286870000,111218000,149632000,20532500,149632000,20532500,0.0651277,0.397202,-0.0163099,-0.0163099,92,"cod_EM_init",2,"h-1" +1,2314010000,2286870000,111218000,149632000,20532500,149632000,20532500,0.0651277,0.397202,-0.01631,-0.01631,92,"cod_EM_init",2,"h-1" 1,2341920000,2311320000,94687000,147414000,20547400,147414000,20547400,0.0635144,0.390413,-0.179503,-0.179503,93,"cod_EM_init",2,"h-1" 1,2352550000,2328100000,135315000,144066000,19529500,144066000,19529500,0.0618715,0.383383,0.175987,0.175987,94,"cod_EM_init",2,"h-1" 1,2346620000,2322150000,92706000,139631000,18272600,139631000,18272600,0.0603863,0.376924,-0.201641,-0.201641,95,"cod_EM_init",2,"h-1" @@ -634,7 +634,7 @@ 1,1934880000,1898990000,113375000,127710000,20854300,127710000,20854300,0.0661971,0.395486,-0.0213972,-0.0213972,100,"cod_EM_103",3,"h-1" 1,1988380000,1964880000,116766000,199326000,30771700,199326000,30771700,0.101969,0.519261,0,0,101,"cod_EM_103",3,"h-1" 1,1967730000,1942730000,116456000,196560000,29267300,196560000,29267300,0.101972,0,-1.11022e-16,0,102,"cod_EM_103",3,"h-1" -1,1938240000,1912650000,116026000,193191000,28425400,193191000,28425400,0.102003,0,0,0,103,"cod_EM_103",3,"h-1" +1,1938240000,1912650000,116026000,193191000,28425400,193191000,28425400,0.102003,0,-1.11022e-16,0,103,"cod_EM_103",3,"h-1" 1,1902390000,1876830000,115501000,189287000,27799000,189287000,27799000,0.101958,0,0,0,104,"cod_EM_103",3,"h-1" 1,1864350000,1838880000,114927000,185424000,27321500,185424000,27321500,0.101958,0,0,0,105,"cod_EM_103",3,"h-1" 1,1827260000,1801910000,114351000,181776000,26938400,181776000,26938400,0.101958,0,0,0,106,"cod_EM_103",3,"h-1" @@ -1263,7 +1263,7 @@ 1,3782660000,3751610000,140957000,0,0,0,0,0,0,0.10553,0.10553,10,"cod_EM_103",5,"h-1" 1,1868700000,1837430000,110613000,127710000,20126700,127710000,20126700,0.0689469,0.406578,-0.029972,-0.029972,100,"cod_EM_103",5,"h-1" 1,1894560000,1876170000,114555000,188810000,27611600,188810000,27611600,0.102043,0.519331,0,0,101,"cod_EM_103",5,"h-1" -1,1852280000,1827980000,113835000,183690000,26410800,183690000,26410800,0.102025,0,-1.11022e-16,0,102,"cod_EM_103",5,"h-1" +1,1852280000,1827980000,113835000,183690000,26410800,183690000,26410800,0.102025,0,0,0,102,"cod_EM_103",5,"h-1" 1,1809230000,1783990000,113152000,179452000,26141600,179452000,26141600,0.102078,0,0,0,103,"cod_EM_103",5,"h-1" 1,1769530000,1744380000,112515000,175565000,25928400,175565000,25928400,0.102009,0,0,0,104,"cod_EM_103",5,"h-1" 1,1735520000,1710510000,111953000,172406000,25745900,172406000,25745900,0.102009,0,0,0,105,"cod_EM_103",5,"h-1" @@ -1288,7 +1288,7 @@ 1,3754640000,3728700000,123227000,297337000,36513100,297337000,36513100,0.0821702,0.455901,0.0180147,0.0180147,27,"cod_EM_103",5,"h-1" 1,3541040000,3511320000,205043000,280706000,33886400,280706000,33886400,0.0824717,0.456954,0.53566,0.53566,28,"cod_EM_103",5,"h-1" 1,3335120000,3302350000,152911000,264545000,32089100,264545000,32089100,0.082722,0.457826,0.251389,0.251389,29,"cod_EM_103",5,"h-1" -1,3836500000,3807730000,126924000,0,0,0,0,0,0,-0.0215131,-0.0215131,3,"cod_EM_103",5,"h-1" +1,3836500000,3807730000,126924000,0,0,0,0,0,0,-0.0215132,-0.0215132,3,"cod_EM_103",5,"h-1" 1,3162370000,3121150000,144337000,252509000,33022900,252509000,33022900,0.0829641,0.458667,0.20248,0.20248,30,"cod_EM_103",5,"h-1" 1,3025450000,2991560000,104143000,243741000,32646700,243741000,32646700,0.0832623,0.4597,-0.117024,-0.117024,31,"cod_EM_103",5,"h-1" 1,2909250000,2879690000,158170000,236189000,31512700,236189000,31512700,0.0837001,0.461212,0.307286,0.307286,32,"cod_EM_103",5,"h-1" @@ -1321,7 +1321,7 @@ 1,1505900000,1483450000,59512800,142016000,21436300,142016000,21436300,0.0967292,0.50349,-0.521269,-0.521269,57,"cod_EM_103",5,"h-1" 1,1485600000,1466060000,102012000,141525000,21322500,141525000,21322500,0.0973451,0.505365,0.0210082,0.0210082,58,"cod_EM_103",5,"h-1" 1,1460260000,1443900000,156779000,138581000,19852900,138581000,19852900,0.0973148,0.505273,0.455163,0.455163,59,"cod_EM_103",5,"h-1" -1,3823250000,3795100000,129772000,0,0,0,0,0,0,0.00976674,0.00976674,6,"cod_EM_103",5,"h-1" +1,3823250000,3795100000,129772000,0,0,0,0,0,0,0.00976669,0.00976669,6,"cod_EM_103",5,"h-1" 1,1438300000,1412630000,63010200,135993000,20346000,135993000,20346000,0.0972933,0.505208,-0.449957,-0.449957,60,"cod_EM_103",5,"h-1" 1,1438550000,1410490000,86645200,138904000,22849400,138904000,22849400,0.0982805,0.508193,-0.130986,-0.130986,61,"cod_EM_103",5,"h-1" 1,1442080000,1425610000,123065000,140169000,21675100,140169000,21675100,0.0987029,0.509462,0.216765,0.216765,62,"cod_EM_103",5,"h-1" @@ -1348,7 +1348,7 @@ 1,1798110000,1782300000,116985000,138723000,17948900,138723000,17948900,0.0792156,0.445421,0.105548,0.105548,81,"cod_EM_103",5,"h-1" 1,1745050000,1723800000,99664100,134467000,17627200,134467000,17627200,0.0794785,0.446366,-0.0462581,-0.0462581,82,"cod_EM_103",5,"h-1" 1,1700290000,1675780000,163760000,131723000,18407900,131723000,18407900,0.0796573,0.447007,0.45764,0.45764,83,"cod_EM_103",5,"h-1" -1,1673500000,1647080000,72811700,129635000,18900500,129635000,18900500,0.0794058,0.446105,-0.348346,-0.348346,84,"cod_EM_103",5,"h-1" +1,1673500000,1647080000,72811600,129635000,18900500,129635000,18900500,0.0794058,0.446105,-0.348346,-0.348346,84,"cod_EM_103",5,"h-1" 1,1676640000,1646860000,81099100,131489000,20778300,131489000,20778300,0.0795598,0.446657,-0.240513,-0.240513,85,"cod_EM_103",5,"h-1" 1,1688170000,1670510000,199492000,132456000,19801400,132456000,19801400,0.0793182,0.44579,0.655837,0.655837,86,"cod_EM_103",5,"h-1" 1,1696320000,1670730000,206749000,131564000,19236200,131564000,19236200,0.0790416,0.444795,0.691532,0.691532,87,"cod_EM_103",5,"h-1" @@ -1424,7 +1424,7 @@ 1,1505900000,1483450000,59512800,142016000,21436300,142016000,21436300,0.0967292,0.50349,-0.521269,-0.521269,57,"cod_EM_init",5,"h-1" 1,1485600000,1466060000,102012000,141525000,21322500,141525000,21322500,0.0973451,0.505365,0.0210082,0.0210082,58,"cod_EM_init",5,"h-1" 1,1460260000,1443900000,156779000,138581000,19852900,138581000,19852900,0.0973148,0.505273,0.455163,0.455163,59,"cod_EM_init",5,"h-1" -1,3823250000,3795100000,129772000,0,0,0,0,0,0,0.00976673,0.00976673,6,"cod_EM_init",5,"h-1" +1,3823250000,3795100000,129772000,0,0,0,0,0,0,0.00976674,0.00976674,6,"cod_EM_init",5,"h-1" 1,1438300000,1412630000,63010200,135993000,20346000,135993000,20346000,0.0972933,0.505208,-0.449957,-0.449957,60,"cod_EM_init",5,"h-1" 1,1438550000,1410490000,86645200,138904000,22849400,138904000,22849400,0.0982805,0.508193,-0.130986,-0.130986,61,"cod_EM_init",5,"h-1" 1,1442080000,1425610000,123065000,140169000,21675100,140169000,21675100,0.0987029,0.509462,0.216765,0.216765,62,"cod_EM_init",5,"h-1" @@ -1894,7 +1894,7 @@ 1,2032400000,1989160000,117716000,127710000,21664200,127710000,21664200,0.0630004,0.389366,-0.0282245,-0.0282245,100,"cod_EM_103",2,"h-ctl" 1,2118040000,2086500000,122443000,207163000,34006900,207163000,34006900,0.0992114,0.519203,0,0,101,"cod_EM_103",2,"h-ctl" 1,2121420000,2.093e+09,122530000,206778000,32090900,206778000,32090900,0.0991982,0,0,0,102,"cod_EM_103",2,"h-ctl" -1,2107360000,2078520000,122335000,204401000,30658300,204401000,30658300,0.0991551,0,-1.11022e-16,0,103,"cod_EM_103",2,"h-ctl" +1,2107360000,2078520000,122335000,204401000,30658300,204401000,30658300,0.0991551,0,0,0,103,"cod_EM_103",2,"h-ctl" 1,2077660000,2048780000,121929000,200992000,29670600,200992000,29670600,0.0992208,0,0,0,104,"cod_EM_103",2,"h-ctl" 1,2037710000,2008880000,121369000,196744000,28913800,196744000,28913800,0.0992208,0,0,0,105,"cod_EM_103",2,"h-ctl" 1,1993450000,1964720000,120730000,192312000,28330800,192312000,28330800,0.0992208,0,0,0,106,"cod_EM_103",2,"h-ctl" @@ -2000,7 +2000,7 @@ 1,2032400000,1989160000,117715000,127710000,21664500,127710000,21664500,0.0630002,0.389365,-0.0282284,-0.0282284,100,"cod_EM_init",2,"h-ctl" 1,2118050000,2086510000,122442000,207184000,34010700,207184000,34010700,0.0992208,0.519231,0,0,101,"cod_EM_init",2,"h-ctl" 1,2121420000,2093010000,122529000,206824000,32098200,206824000,32098200,0.0992208,0,0,0,102,"cod_EM_init",2,"h-ctl" -1,2107330000,2078480000,122334000,204527000,30677400,204527000,30677400,0.0992208,0,0,0,103,"cod_EM_init",2,"h-ctl" +1,2107330000,2078480000,122334000,204527000,30677400,204527000,30677400,0.0992208,0,-1.11022e-16,0,103,"cod_EM_init",2,"h-ctl" 1,3953410000,3923780000,122786000,0,0,0,0,0,0,-0.0817835,-0.0817835,11,"cod_EM_init",2,"h-ctl" 1,3947330000,3914060000,142141000,0,0,0,0,0,0,0.0678122,0.0678122,12,"cod_EM_init",2,"h-ctl" 1,3941460000,3910370000,172346000,0,0,0,0,0,0,0.263504,0.263504,13,"cod_EM_init",2,"h-ctl" @@ -2032,7 +2032,7 @@ 1,2557030000,2532270000,63643800,203134000,26635000,203134000,26635000,0.0818779,0.462733,-0.623472,-0.623472,37,"cod_EM_init",2,"h-ctl" 1,2479770000,2445050000,176749000,198280000,27412200,198280000,27412200,0.0823121,0.464264,0.404945,0.404945,38,"cod_EM_init",2,"h-ctl" 1,2399520000,2374670000,74609200,191343000,25443800,191343000,25443800,0.0822212,0.463944,-0.451548,-0.451548,39,"cod_EM_init",2,"h-ctl" -1,4011990000,3979870000,132703000,0,0,0,0,0,0,-0.0261969,-0.0261969,4,"cod_EM_init",2,"h-ctl" +1,4011990000,3979870000,132703000,0,0,0,0,0,0,-0.026197,-0.026197,4,"cod_EM_init",2,"h-ctl" 1,2327340000,2294340000,90569700,187276000,26117300,187276000,26117300,0.082839,0.466114,-0.250475,-0.250475,40,"cod_EM_init",2,"h-ctl" 1,2251940000,2231800000,87445700,181328000,24202200,181328000,24202200,0.082817,0.466037,-0.279636,-0.279636,41,"cod_EM_init",2,"h-ctl" 1,2166450000,2145400000,283935000,174744000,22605700,174744000,22605700,0.0833025,0.467734,0.906803,0.906803,42,"cod_EM_init",2,"h-ctl" @@ -2310,31 +2310,31 @@ 1,1777850000,1760750000,130647000,131416000,17501200,131416000,17501200,0.0756515,0.433058,0.222823,0.222823,97,"cod_EM_103",3,"h-ctl" 1,1725350000,1704800000,160543000,127005000,17075000,127005000,17075000,0.0755399,0.432641,0.437045,0.437045,98,"cod_EM_103",3,"h-ctl" 1,1693490000,1663890000,93381800,126234000,18920700,126234000,18920700,0.0760138,0.43441,-0.0985533,-0.0985533,99,"cod_EM_103",3,"h-ctl" -1,3794520000,3766200000,127595000,0,0,0,0,0,0,-0.0154639,-0.0154639,1,"cod_EM_init",3,"h-ctl" +1,3794520000,3766200000,127595000,0,0,0,0,0,0,-0.015464,-0.015464,1,"cod_EM_init",3,"h-ctl" 1,3737120000,3707090000,140316000,0,0,0,0,0,0,0.107636,0.107636,10,"cod_EM_init",3,"h-ctl" -1,1698100000,1667990000,110497000,127710000,21041100,127710000,21041100,0.0755447,0.432658,-0.00290045,-0.00290045,100,"cod_EM_init",3,"h-ctl" -1,1722740000,1700690000,111375000,172381000,27093200,172381000,27093200,0.101653,0.519231,-1.11022e-16,0,101,"cod_EM_init",3,"h-ctl" +1,1698100000,1667990000,110497000,127710000,21041100,127710000,21041100,0.0755447,0.432658,-0.00290033,-0.00290033,100,"cod_EM_init",3,"h-ctl" +1,1722740000,1700690000,111375000,172381000,27093200,172381000,27093200,0.101653,0.519231,0,0,101,"cod_EM_init",3,"h-ctl" 1,1707290000,1683500000,111084000,170746000,26544300,170746000,26544300,0.101653,0,0,0,102,"cod_EM_init",3,"h-ctl" 1,1691570000,1667550000,110811000,169099000,26229900,169099000,26229900,0.101653,0,0,0,103,"cod_EM_init",3,"h-ctl" -1,3728990000,3703700000,115225000,0,0,0,0,0,0,-0.0863726,-0.0863726,11,"cod_EM_init",3,"h-ctl" +1,3728990000,3703700000,115225000,0,0,0,0,0,0,-0.0863725,-0.0863725,11,"cod_EM_init",3,"h-ctl" 1,3722460000,3693810000,133480000,0,0,0,0,0,0,0.0639367,0.0639367,12,"cod_EM_init",3,"h-ctl" 1,3715870000,3689250000,162370000,0,0,0,0,0,0,0.262912,0.262912,13,"cod_EM_init",3,"h-ctl" 1,3713170000,3682370000,113494000,0,0,0,0,0,0,-0.0920773,-0.0920773,14,"cod_EM_init",3,"h-ctl" 1,3723650000,3691640000,94928400,0,0,0,0,0,0,-0.26817,-0.26817,15,"cod_EM_init",3,"h-ctl" 1,3737620000,3713400000,99787500,0,0,0,0,0,0,-0.216173,-0.216173,16,"cod_EM_init",3,"h-ctl" 1,3737280000,3715930000,160020000,0,0,0,0,0,0,0.25887,0.25887,17,"cod_EM_init",3,"h-ctl" -1,3718140000,3692540000,170343000,0,0,0,0,0,0,0.325128,0.325128,18,"cod_EM_init",3,"h-ctl" +1,3718140000,3692540000,170343000,0,0,0,0,0,0,0.325129,0.325129,18,"cod_EM_init",3,"h-ctl" 1,3701730000,3666870000,141909000,0,0,0,0,0,0,0.14634,0.14634,19,"cod_EM_init",3,"h-ctl" 1,3794400000,3766200000,126829000,0,0,0,0,0,0,-0.0186107,-0.0186107,2,"cod_EM_init",3,"h-ctl" 1,3709090000,3673860000,102844000,0,0,0,0,0,0,-0.173015,-0.173015,20,"cod_EM_init",3,"h-ctl" 1,3737270000,3708390000,152852000,0,0,0,0,0,0,0.224841,0.224841,21,"cod_EM_init",3,"h-ctl" -1,3767070000,3740900000,172543000,0,0,0,0,0,0,0.347706,0.347706,22,"cod_EM_init",3,"h-ctl" +1,3767070000,3740900000,172542000,0,0,0,0,0,0,0.347706,0.347706,22,"cod_EM_init",3,"h-ctl" 1,3798370000,3764300000,193677000,0,0,0,0,0,0,0.465294,0.465294,23,"cod_EM_init",3,"h-ctl" 1,3844680000,3805930000,220575000,0,0,0,0,0,0,0.596743,0.596743,24,"cod_EM_init",3,"h-ctl" 1,3921640000,3877910000,102585000,0,0,0,0,0,0,-0.168403,-0.168403,25,"cod_EM_init",3,"h-ctl" 1,4034180000,3993820000,144685000,313240000,41916000,313240000,41916000,0.079934,0.448736,0.17453,0.17453,26,"cod_EM_init",3,"h-ctl" 1,3833210000,3806950000,126411000,297337000,37108500,297337000,37108500,0.0801311,0.449442,0.0457721,0.0457721,27,"cod_EM_init",3,"h-ctl" -1,3627750000,3597940000,210008000,280706000,34665800,280706000,34665800,0.0800929,0.449306,0.561093,0.561093,28,"cod_EM_init",3,"h-ctl" +1,3627750000,3597940000,210008000,280706000,34665800,280706000,34665800,0.080093,0.449306,0.561093,0.561093,28,"cod_EM_init",3,"h-ctl" 1,3424380000,3391530000,154123000,264545000,32669500,264545000,32669500,0.0802123,0.449733,0.260185,0.260185,29,"cod_EM_init",3,"h-ctl" 1,3793440000,3765570000,125965000,0,0,0,0,0,0,-0.0225396,-0.0225396,3,"cod_EM_init",3,"h-ctl" 1,3252690000,3211720000,145620000,252509000,34063600,252509000,34063600,0.0800811,0.449263,0.211646,0.211646,30,"cod_EM_init",3,"h-ctl" @@ -2354,14 +2354,14 @@ 1,2012290000,1981930000,35002400,167425000,22395600,167425000,22395600,0.0863692,0.471126,-1.12252,-1.12252,43,"cod_EM_init",3,"h-ctl" 1,1979200000,1936920000,59208100,169673000,29042800,169673000,29042800,0.0864512,0.471402,-0.591591,-0.591591,44,"cod_EM_init",3,"h-ctl" 1,1961260000,1949560000,174276000,168561000,24525900,168561000,24525900,0.0870433,0.473393,0.486485,0.486485,45,"cod_EM_init",3,"h-ctl" -1,1923750000,1903710000,106786000,163795000,22066500,163795000,22066500,0.0873268,0.474343,0.00221694,0.00221694,46,"cod_EM_init",3,"h-ctl" +1,1923750000,1903710000,106786000,163795000,22066500,163795000,22066500,0.0873268,0.474343,0.00221693,0.00221693,46,"cod_EM_init",3,"h-ctl" 1,1892060000,1859960000,65813800,162330000,24796700,162330000,24796700,0.0872902,0.47422,-0.476257,-0.476257,47,"cod_EM_init",3,"h-ctl" 1,1867750000,1846590000,102972000,161850000,24204000,161850000,24204000,0.0879317,0.47636,-0.0269002,-0.0269002,48,"cod_EM_init",3,"h-ctl" 1,1831520000,1814410000,121345000,158898000,22038900,158898000,22038900,0.0886634,0.478786,0.141548,0.141548,49,"cod_EM_init",3,"h-ctl" -1,3786260000,3758780000,123857000,0,0,0,0,0,0,-0.0334167,-0.0334167,5,"cod_EM_init",3,"h-ctl" +1,3786260000,3758780000,123857000,0,0,0,0,0,0,-0.0334168,-0.0334168,5,"cod_EM_init",3,"h-ctl" 1,1786740000,1763680000,69469300,155942000,22046700,155942000,22046700,0.089419,0.481273,-0.409211,-0.409211,50,"cod_EM_init",3,"h-ctl" 1,1744970000,1722250000,93658600,155270000,22862600,155270000,22862600,0.0908002,0.485775,-0.104459,-0.104459,51,"cod_EM_init",3,"h-ctl" -1,1698140000,1681010000,86405100,152386000,21328000,152386000,21328000,0.0919469,0.489469,-0.178861,-0.178861,52,"cod_EM_init",3,"h-ctl" +1,1698140000,1681010000,86405100,152386000,21328000,152386000,21328000,0.091947,0.489469,-0.178861,-0.178861,52,"cod_EM_init",3,"h-ctl" 1,1647100000,1627530000,77688900,149613000,21130000,149613000,21130000,0.0931736,0.493377,-0.276738,-0.276738,53,"cod_EM_init",3,"h-ctl" 1,1593820000,1575630000,121108000,146908000,20747100,146908000,20747100,0.0946076,0.497891,0.175912,0.175912,54,"cod_EM_init",3,"h-ctl" 1,1540320000,1520670000,94256200,143617000,20312000,143617000,20312000,0.0959758,0.502143,-0.0650047,-0.0650047,55,"cod_EM_init",3,"h-ctl" @@ -2369,7 +2369,7 @@ 1,1473080000,1452020000,56580200,142016000,21956300,142016000,21956300,0.0982738,0.509166,-0.562298,-0.562298,57,"cod_EM_init",3,"h-ctl" 1,1453320000,1434340000,92978700,141525000,21940800,141525000,21940800,0.0988908,0.511027,-0.0620502,-0.0620502,58,"cod_EM_init",3,"h-ctl" 1,1426090000,1411050000,153593000,138581000,19994600,138581000,19994600,0.099363,0.512444,0.444662,0.444662,59,"cod_EM_init",3,"h-ctl" -1,3779230000,3751970000,128810000,0,0,0,0,0,0,0.0089186,0.0089186,6,"cod_EM_init",3,"h-ctl" +1,3779230000,3751970000,128810000,0,0,0,0,0,0,0.00891867,0.00891867,6,"cod_EM_init",3,"h-ctl" 1,1398520000,1374970000,62345700,135993000,20538300,135993000,20538300,0.0996707,0.513364,-0.449282,-0.449282,60,"cod_EM_init",3,"h-ctl" 1,1392170000,1365490000,82677800,138904000,23643000,138904000,23643000,0.100747,0.516562,-0.164953,-0.164953,61,"cod_EM_init",3,"h-ctl" 1,1389150000,1373420000,120583000,140169000,21889400,140169000,21889400,0.102278,0.521059,0.210699,0.210699,62,"cod_EM_init",3,"h-ctl" @@ -2405,14 +2405,14 @@ 1,3745760000,3718750000,101530000,0,0,0,0,0,0,-0.219214,-0.219214,9,"cod_EM_init",3,"h-ctl" 1,1854290000,1833240000,88214200,150269000,25656500,150269000,25656500,0.079744,0.448054,-0.179834,-0.179834,90,"cod_EM_init",3,"h-ctl" 1,1919100000,1903990000,130683000,151592000,22195800,151592000,22195800,0.0790207,0.445446,0.204131,0.204131,91,"cod_EM_init",3,"h-ctl" -1,1935650000,1914140000,95717700,149632000,20849000,149632000,20849000,0.0782969,0.442818,-0.108487,-0.108487,92,"cod_EM_init",3,"h-ctl" -1,1928410000,1902840000,77620100,147414000,21322000,147414000,21322000,0.0774795,0.439828,-0.316674,-0.316674,93,"cod_EM_init",3,"h-ctl" +1,1935650000,1914140000,95717600,149632000,20849000,149632000,20849000,0.0782969,0.442818,-0.108487,-0.108487,92,"cod_EM_init",3,"h-ctl" +1,1928410000,1902840000,77620100,147414000,21322000,147414000,21322000,0.0774795,0.439828,-0.316673,-0.316673,93,"cod_EM_init",3,"h-ctl" 1,1907710000,1887770000,96609400,144066000,20319000,144066000,20319000,0.0766699,0.436844,-0.0959455,-0.0959455,94,"cod_EM_init",3,"h-ctl" 1,1872890000,1854630000,77053800,139631000,18905600,139631000,18905600,0.0760523,0.434552,-0.317895,-0.317895,95,"cod_EM_init",3,"h-ctl" 1,1829390000,1809900000,80195700,136395000,18563000,136395000,18563000,0.0761364,0.434865,-0.272014,-0.272014,96,"cod_EM_init",3,"h-ctl" 1,1777850000,1760750000,130648000,131416000,17501200,131416000,17501200,0.0756514,0.433057,0.222823,0.222823,97,"cod_EM_init",3,"h-ctl" 1,1725350000,1704800000,160543000,127005000,17075000,127005000,17075000,0.0755399,0.432639,0.437044,0.437044,98,"cod_EM_init",3,"h-ctl" -1,1693490000,1663890000,93381200,126234000,18920800,126234000,18920800,0.0760137,0.434408,-0.0985609,-0.0985609,99,"cod_EM_init",3,"h-ctl" +1,1693490000,1663890000,93381200,126234000,18920800,126234000,18920800,0.0760137,0.434408,-0.098561,-0.098561,99,"cod_EM_init",3,"h-ctl" 1,3908680000,3878510000,131395000,0,0,0,0,0,NA,-0.018531,-0.018531,1,"cod_OM",3,"h-ctl" 1,3843390000,3811600000,144385000,0,0,0,0,0,NA,0.104019,0.104019,10,"cod_OM",3,"h-ctl" 1,1932130000,1897560000,155187000,127710000,20989800,127710000,20989800,0.0662443,NA,0.279384,0.279384,100,"cod_OM",3,"h-ctl" @@ -2522,9 +2522,9 @@ 1,3961360000,3930330000,136325000,0,0,0,0,0,0,-0.0227761,-0.0227761,1,"cod_EM_103",4,"h-ctl" 1,3881500000,3849270000,148189000,0,0,0,0,0,0,0.0894241,0.0894241,10,"cod_EM_103",4,"h-ctl" 1,2277270000,2237600000,124861000,127710000,21021900,127710000,21021900,0.0560794,0.350218,-0.0136419,-0.0136419,100,"cod_EM_103",4,"h-ctl" -1,2338750000,2312820000,127465000,234981000,36287900,234981000,36287900,0.102662,0.519211,0,0,101,"cod_EM_103",4,"h-ctl" -1,2285240000,2257760000,126819000,228546000,34100200,228546000,34100200,0.102569,0,0,0,102,"cod_EM_103",4,"h-ctl" -1,2221850000,2193940000,126037000,221615000,32722800,221615000,32722800,0.10255,0,-1.11022e-16,0,103,"cod_EM_103",4,"h-ctl" +1,2338750000,2312820000,127465000,234981000,36287900,234981000,36287900,0.102662,0.519211,-1.11022e-16,0,101,"cod_EM_103",4,"h-ctl" +1,2285240000,2257760000,126819000,228546000,34100200,228546000,34100200,0.102569,0,-1.11022e-16,0,102,"cod_EM_103",4,"h-ctl" +1,2221850000,2193940000,126037000,221615000,32722800,221615000,32722800,0.10255,0,0,0,103,"cod_EM_103",4,"h-ctl" 1,2153040000,2125230000,125156000,214732000,31727700,214732000,31727700,0.102668,0,0,0,104,"cod_EM_103",4,"h-ctl" 1,2084180000,2056550000,124229000,207843000,30916900,207843000,30916900,0.102668,0,0,0,105,"cod_EM_103",4,"h-ctl" 1,2019650000,1992220000,123316000,201538000,30265900,201538000,30265900,0.102668,0,0,0,106,"cod_EM_103",4,"h-ctl" @@ -2944,7 +2944,7 @@ 1,3810860000,3779610000,148211000,0,0,0,0,0,0,0.119833,0.119833,10,"cod_EM_init",5,"h-ctl" 1,2027950000,1995630000,119093000,127710000,21181800,127710000,21181800,0.0627067,0.376809,-0.00993507,-0.00993507,100,"cod_EM_init",5,"h-ctl" 1,2077460000,2057540000,121120000,210848000,31769400,210848000,31769400,0.103544,0.519231,0,0,101,"cod_EM_init",5,"h-ctl" -1,2030320000,2005400000,120417000,205235000,30459700,205235000,30459700,0.103544,0,0,0,102,"cod_EM_init",5,"h-ctl" +1,2030320000,2005400000,120417000,205235000,30459700,205235000,30459700,0.103544,0,-1.11022e-16,0,102,"cod_EM_init",5,"h-ctl" 1,1975800000,1950300000,119642000,199470000,29761400,199470000,29761400,0.103544,0,0,0,103,"cod_EM_init",5,"h-ctl" 1,3805300000,3779350000,121599000,0,0,0,0,0,0,-0.0751879,-0.0751879,11,"cod_EM_init",5,"h-ctl" 1,3801610000,3771870000,141093000,0,0,0,0,0,0,0.0766514,0.0766514,12,"cod_EM_init",5,"h-ctl" diff --git a/man/figures/README-plot_SSB-1.png b/man/figures/README-plot_SSB-1.png index cfb480aa..e0528ea1 100644 Binary files a/man/figures/README-plot_SSB-1.png and b/man/figures/README-plot_SSB-1.png differ diff --git a/man/figures/README-plot_SSB_avg-1.png b/man/figures/README-plot_SSB_avg-1.png index 7a0813af..1d32afc1 100644 Binary files a/man/figures/README-plot_SSB_avg-1.png and b/man/figures/README-plot_SSB_avg-1.png differ