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mode 100644 rfaR-book/cover.jpg create mode 100644 rfaR-book/generate_reference.R create mode 100644 rfaR-book/index.qmd create mode 100644 rfaR-book/references.bib diff --git a/05-function-reference.qmd b/05-function-reference.qmd new file mode 100644 index 0000000..82f4a76 --- /dev/null +++ b/05-function-reference.qmd @@ -0,0 +1,1224 @@ +# Function Reference + +## Execute Reservoir Frequency Analysis + +### `rfa_simulate()` + +``` +_R_F_A _S_t_a_g_e-_F_r_e_q_u_e_n_c_y _S_i_m_u_l_a_t_i_o_n + +_D_e_s_c_r_i_p_t_i_o_n: + + Performs reservoir stage frequency analysis using Monte Carlo + simulation to develop stage-frequency relationships. Combines + stratified flow-frequency sampling, flood seasonality, hydrograph + scaling, and Modified Puls reservoir routing to estimate annual + exceedance probabilities of reservoir stage. + +_U_s_a_g_e: + + rfa_simulate( + sim_type = "expected", + bestfit_params, + dist = "LP3", + stage_ts, + seasonality, + hydrographs, + resmodel, + Nbins = 50, + events_per_bin = 200, + routing_dt = 1, + Ncores = NULL, + sim_name = NULL, + results_dir = NULL + ) + +_A_r_g_u_m_e_n_t_s: + +sim_type: Character string specifying the simulation mode. One of + ‘"median"’, ‘"expected"’, or ‘"full"’. Default is + ‘"expected"’. + +bestfit_params: Data frame or matrix of distribution parameters from + RMC-BestFit MCMC output. Columns 1-3 are distribution + parameters: + + * LP3: mean (log), standard deviation (log), skew (log) + + * GEV: location, scale, shape + + Column 4 is log-likelihood (used only in ‘"median"’ mode to + identify the posterior mode). For ‘"expected"’ mode, must + have at least ‘Nbins * events_per_bin’ rows. For ‘"full"’ + mode, each row is treated as an independent realization. + + dist: Character string specifying the frequency distribution. + Either ‘"LP3"’ (Log-Pearson Type III, default) or ‘"GEV"’ + (Generalized Extreme Value). + +stage_ts: Data frame of historical reservoir stage with columns ‘date’ + (character in M/D/YYYY format) and ‘stage’ (numeric, feet). + +seasonality: Numeric vector of length 12 giving monthly flood + occurrence probabilities (relative frequencies). Used to + sample the month of each simulated event, which determines + the antecedent pool elevation. + +hydrographs: List of hydrograph data frames as returned by + ‘hydrograph_setup’. Each element has columns ‘datetime’, + ‘hour’, ‘inflow’, and ‘hydrograph_num’, with attributes + ‘"obs_vol"’ (observed max n-day volume) and ‘"dt"’ (timestep + in hours). The list must have a ‘"probs"’ attribute + containing normalized sampling probabilities. + +resmodel: Data frame with three columns: elevation (ft), storage + (acre-ft), and discharge (cfs). Defines the reservoir model + for Modified Puls routing. + + Nbins: Integer. Number of stratified sampling bins in EV1 space. + Default is 50. + +events_per_bin: Integer. Number of events sampled per bin. Default is + 200. Total simulations per realization = ‘Nbins * + events_per_bin’. + +routing_dt: Numeric. Routing timestep in hours. Passed to + ‘scale_hydrograph’ to resample hydrographs before routing. + Supported values are ‘0.25’ (15-min), ‘1’ (1-hour, default), + ‘6’ (6-hour), and ‘24’ (24-hour). + + Ncores: Integer or ‘NULL’. Number of parallel workers for ‘"full"’ + mode. If ‘NULL’ (default), automatically selects based on + available cores (capped at 16). Ignored for ‘"median"’ and + ‘"expected"’ modes. + +sim_name: Optional character string to label the simulation. Used in + the output CSV filename. If ‘NULL’ (default), defaults to + ‘"sim"’. Spaces are replaced with underscores in the + filename. + +results_dir: Optional path to the directory where results are saved. If + ‘NULL’ (default), creates an ‘rfaR_results’ folder in the + current working directory. + +_D_e_t_a_i_l_s: + + Three simulation modes are available: + + ‘"median"’ Single realization using the most likely (posterior + mode) parameter set from RMC-BestFit. Produces one + stage-frequency curve with no uncertainty bounds. Useful for + quick screening or debugging. + + ‘"expected"’ Single realization using expected value sampling, + where each stratified flow sample is paired 1-to-1 with a + different parameter set from the posterior distribution. + Produces one stage-frequency curve representing the expected + (mean) result integrated across parameter uncertainty. + + ‘"full"’ Nested Monte Carlo with full uncertainty quantification. + The outer loop iterates over all parameter sets (one per row + of ‘bestfit_params’), and each inner loop independently + samples natural variability via stratified sampling, + seasonality, starting pool, and hydrograph shape. Each + realization produces an independent stage-frequency curve; + curves are combined on a common stage grid to yield expected, + median, and 5th/95th percentile confidence bounds. The outer + loop is parallelized using the ‘future’ framework. + + For all modes, natural variability is captured through stratified + sampling in with ‘Nbins’ bins and ‘events_per_bin’ events per bin, + providing reliable coverage from common events down to + approximately 1e-8 AEP. + + The stratified sampling approach divides the AEP range (default + 0.99 to 1e-8) into equal-width bins in EV1 (Gumbel reduced + variate) space and samples events uniformly within each bin. This + ensures adequate representation of rare flood events that would + require orders of magnitude more samples under crude Monte Carlo + sampling. + + Post-processing uses the law of total probability via + ‘stage_frequency_curve’: for a grid of stage thresholds, the + weighted exceedance fraction is computed within each bin and + summed across bins. + + In ‘"full"’ mode, each parallel worker independently samples all + random inputs (seasonality, starting pool, hydrograph shape, and + stratified z-variates) in addition to using its own parameter set. + This ensures full Monte Carlo independence across realizations. + Each worker post-processes its results into a stage-frequency + curve immediately, returning only the curve rather than the full + peak stage matrix, keeping memory usage manageable for large + numbers of realizations. + + Curves from individual realizations are combined by interpolating + each onto a common stage grid (spanning the global min/max across + all realizations) in log10(AEP) space, then computing summary + statistics across realizations at each stage threshold. + + Results are automatically exported as a CSV file named + ‘{sim_name}_{sim_type}_{MM_DD_YY_HHMM}.csv’ in the ‘results_dir’ + directory. For example, a median simulation named "John McGraw + Dam" run on June 4, 2025 at 2:30 PM would produce + ‘John_McGraw_Dam_median_06_04_25_1430.csv’. + +_V_a_l_u_e: + + A list whose contents depend on ‘sim_type’: + + For ‘"median"’ and ‘"expected"’: + + stage_frequency Data frame with columns ‘stage’ and ‘AEP’ from + ‘stage_frequency_curve’. + + peakStage Matrix of simulated peak stages (‘events_per_bin’ rows + by ‘Nbins’ columns). + + peakFlow Matrix of simulated peak discharges (same dimensions). + + weights Stratified sampling bin weights. + + For ‘"full"’: + + stage_frequency Data frame with columns ‘stage’, ‘expected’, + ‘median’, ‘lower_05’, and ‘upper_95’ on a common stage grid. + + all_curves List of per-realization stage-frequency data frames + (each with ‘stage’ and ‘AEP’ columns). + + aep_matrix Matrix of interpolated AEP values (500 rows by + ‘Nrealizations’ columns) on the common stage grid. + + common_stage Numeric vector of the common stage grid (length 500). + + Nrealizations Number of parameter set realizations processed. + + Nsims_per_realiz Number of simulations per realization. + +_S_e_e _A_l_s_o: + + ‘flow_frequency_sampler’, ‘flow_frequency_sampler_expected’, + ‘stratified_sampler’, ‘scale_hydrograph’, ‘mod_puls_routing’, + ‘stage_frequency_curve’, ‘hydrograph_setup’ + +_E_x_a_m_p_l_e_s: + + ## Not run: + + # Not run: Expected and Median modes take ~15s; Full uncert + # runs ~10,000 parameter realizations and takes 3-4 hours on a + # typical machine. + + # --- Setup --- + hydros <- hydrograph_setup(jmd_hydro_apr1999, jmd_hydro_jun1965, + jmd_hydro_may1955, jmd_hydro_pmf, + critical_duration = 2, routing_days = 10) + + # --- Expected only (default) --- + results_exp <- rfa_simulate( + sim_type = "expected", + bestfit_params = jmd_bf_parameter_sets, + stage_ts = jmd_wy1980_stage, + seasonality = jmd_seasonality$relative_frequency, + hydrographs = hydros, + resmodel = jmd_resmodel, + sim_name = "jmd" + ) + + # --- Median only --- + results_med <- rfa_simulate( + sim_type = "median", + bestfit_params = jmd_bf_parameter_sets, + stage_ts = jmd_wy1980_stage, + seasonality = jmd_seasonality$relative_frequency, + hydrographs = hydros, + resmodel = jmd_resmodel, + sim_name = "jmd" + ) + + # --- Full uncertainty (parallelized) --- + results_full <- rfa_simulate( + sim_type = "full", + bestfit_params = jmd_bf_parameter_sets, + stage_ts = jmd_wy1980_stage, + seasonality = jmd_seasonality$relative_frequency, + hydrographs = hydros, + resmodel = jmd_resmodel, + Ncores = 4, + sim_name = "jmd" + ) + ## End(Not run) + +``` + +## Primary RFA Modules + +### `stratified_sampler()` + +``` +_S_t_r_a_t_i_f_i_e_d _S_a_m_p_l_e_r + +_D_e_s_c_r_i_p_t_i_o_n: + + Creates stratified bins in standard normal space for use in + stratified sampling. Stratification improves sampling efficiency + by ensuring adequate coverage of rare events. + +_U_s_a_g_e: + + stratified_sampler( + minAEP = 1e-08, + maxAEP = 0.99, + dist = "EV1", + Nbins = NULL, + Mevents = NULL, + verbose = FALSE + ) + +_A_r_g_u_m_e_n_t_s: + + minAEP: Minimum annual exceedance probability. Default is ‘1E-8’. + + maxAEP: Maximum annual exceedance probability. Default is ‘0.99’. + + dist: Character. Probability space for stratification. + Case-insensitive. Default is ‘"EV1"’. Invalid values trigger + a warning and default to ‘"EV1"’. + + ‘"EV1"’ Extreme Value Type I (Gumbel) space. Recommended for + flood frequency analysis. Allocates more bins to rare + events through the transformation ‘-log(-log(1-AEP))’, + improving tail estimation efficiency. + + ‘"Normal"’ Standard normal (z-score) space. Uniform bins in + z-space. Use when the underlying phenomenon is normally + distributed. + + ‘"Uniform"’ Uniform probability space. Equal probability + width per bin. Generally inefficient for rare event + estimation. + + Nbins: Number of stratified bins. Default is ‘20’. + + Mevents: Number of events per bin. Default is ‘500’. + + verbose: Logical. If ‘TRUE’, prints a completion message summarizing + the stratification. Default is ‘FALSE’. + +_D_e_t_a_i_l_s: + + The function divides the probability space into bins using the + specified transformation. EV1 transformation is recommended for + heavy-tailed distributions common in flood frequency analysis, as + it naturally allocates more sampling effort to rare events + critical for dam safety assessments. + +_V_a_l_u_e: + + A list containing: + + normOrd Vector of standard normal ordinates spanning all bins + + Weights Vector of probability weights for each bin + + Zlower Vector of lower bounds for each bin (standard normal) + + Zupper Vector of upper bounds for each bin (standard normal) + + Nbins Number of bins + + Mevents Number of events per bin + +_E_x_a_m_p_l_e_s: + + # Default stratification + strat <- stratified_sampler() + + # Custom bins and events + strat <- stratified_sampler(minAEP = 1E-6, + maxAEP = 0.5, + dist = "EV1", + Nbins = 10, + Mevents = 100) + + # With verbose message + strat <- stratified_sampler(verbose = TRUE) + +``` + +### `flow_frequency_sampler()` + +``` +_F_l_o_w _F_r_e_q_u_e_n_c_y _S_a_m_p_l_e_r + +_D_e_s_c_r_i_p_t_i_o_n: + + Generates a stratified matrix of flow values from a single set of + frequency distribution parameters using stratified Monte Carlo + sampling. Used internally by ‘rfa_simulate()’ for median-only and + full uncertainty modes. + +_U_s_a_g_e: + + flow_frequency_sampler( + bestfit_params, + freq_dist = "LP3", + strat_dist = "ev1", + Nbin = NULL, + Mevent = NULL + ) + +_A_r_g_u_m_e_n_t_s: + +bestfit_params: Numeric vector of length 3 containing distribution + parameters. For LP3: ‘c(mean_log, sd_log, skew_log)’. For + GEV: ‘c(location, scale, shape)’. + +freq_dist: Character. Distribution type. Either ‘"LP3"’ (default) or + ‘"GEV"’. + +strat_dist: Character. Probability space for stratification bins. + Passed to ‘stratified_sampler()’. One of ‘"ev1"’ (default), + ‘"normal"’, or ‘"uniform"’. See ‘stratified_sampler()’ for + details. + + Nbin: Integer. Number of stratified bins. Default is ‘50’. + + Mevent: Integer. Number of events per bin. Default is ‘200’. + +_V_a_l_u_e: + + A list containing: + + flow Matrix of sampled flow values [Mevent x Nbin] + + nbins Number of stratified bins + + mevents Number of events per bin + + weights Probability weights for each bin from + ‘stratified_sampler()’ + +_S_e_e _A_l_s_o: + + ‘stratified_sampler()’, ‘qp3()’, ‘rfa_simulate()’ + +_E_x_a_m_p_l_e_s: + + # Single parameter set (posterior mode) + params <- c(4.85, 0.39, -0.15) + result <- flow_frequency_sampler(params, freq_dist = "LP3", + Nbin = 20, Mevent = 500) + # Dimensions of result + dim(result$flow) # 500 x 20 + + # Distribution of Sampled Flows + hist(result$flow) + +``` + +### `flow_frequency_sampler_expected()` + +``` +_F_l_o_w _F_r_e_q_u_e_n_c_y _S_a_m_p_l_e_r (_E_x_p_e_c_t_e_d _O_n_l_y) + +_D_e_s_c_r_i_p_t_i_o_n: + + Generates a stratified matrix of flow values by pairing each + z-ordinate with a different parameter set from the posterior + distribution. This collapses the nested Monte Carlo structure into + a single pass, simultaneously sampling natural variability (via + stratified z-ordinates) and knowledge uncertainty (via varying + parameters). Used internally by ‘rfa_simulate()’ for expected-only + mode. + +_U_s_a_g_e: + + flow_frequency_sampler_expected( + bestfit_params, + freq_dist = "LP3", + strat_dist = "ev1", + Nbin = NULL, + Mevent = NULL + ) + +_A_r_g_u_m_e_n_t_s: + +bestfit_params: Data frame of distribution parameters from RMC-BestFit. + Must have ‘Nbin * Mevent’ rows (one parameter set per + z-ordinate). For LP3: columns are mean (log), sd (log), skew + (log). For GEV: columns are location, scale, shape. + +freq_dist: Character. Distribution type. Either ‘"LP3"’ (default) or + ‘"GEV"’. + +strat_dist: Character. Probability space for stratification bins. + Passed to ‘stratified_sampler()’. One of ‘"ev1"’ (default), + ‘"normal"’, or ‘"uniform"’. See ‘stratified_sampler()’ for + details. + + Nbin: Integer. Number of stratified bins. Default is ‘50’. + + Mevent: Integer. Number of events per bin. Default is ‘200’. + +_V_a_l_u_e: + + A list containing: + + flow Matrix of sampled flow values [Mevent x Nbin] + + nbins Number of stratified bins + + mevents Number of events per bin + + weights Probability weights for each bin from + ‘stratified_sampler()’ + +_S_e_e _A_l_s_o: + + ‘stratified_sampler()’, ‘qp3()’, ‘flow_frequency_sampler()’, + ‘rfa_simulate()’ + +_E_x_a_m_p_l_e_s: + + # Using a pre-loaded parameter set (all 10,000 parameter sets) + result <- flow_frequency_sampler_expected(jmd_bf_parameter_sets, + Nbin = 20, Mevent = 500) + dim(result$flow) # 500 x 20 + + # Using bootstrapped parameter samples + jmd_samples <- bootstrap_vfc( + c(jmd_vfc_parameters$mean_log, + jmd_vfc_parameters$sd_log, + jmd_vfc_parameters$skew_log), + dist = "LP3", + ERL = jmd_vfc_parameters$erl) + + jmd_result <- flow_frequency_sampler_expected( + jmd_samples$params, + freq_dist = "LP3", + Nbin = 20, + Mevent = 500) + +``` + +### `hydrograph_setup()` + +``` +_H_y_d_r_o_g_r_a_p_h _S_e_t_u_p _f_o_r _R_F_A _S_i_m_u_l_a_t_i_o_n + +_D_e_s_c_r_i_p_t_i_o_n: + + Prepares hydrograph data frames copied from RMC-RFA for use in RFA + simulation. Converts date/time columns and adds sequential hour + and hydrograph ID columns. + +_U_s_a_g_e: + + hydrograph_setup( + ..., + critical_duration = NULL, + routing_days = NULL, + weights = NULL + ) + +_A_r_g_u_m_e_n_t_s: + + ...: Data frame representing an input hydrograph with columns: + Ord, Date, Time, Flow (cfs). Copied directly from RMC-RFA. + Date & Time should be ‘class() = "character"’ + +critical_duration: Critical duration in days. + +routing_days: Desired length of routing simulation in days. + + weights: Optional numeric vector of sampling weights for each + hydrograph. Must be the same length as the number of input + hydrographs. Weights are normalized to probabilities + internally. If NULL (default), all hydrographs are weighted + equally. + +_V_a_l_u_e: + + A list of formatted hydrograph data frames, each containing: + + datetime Date-time (POSIXct) + + hour Hours from start of event + + inflow Inflow (cfs) + + hydrograph_num Hydrograph ID number for sampling + + obs_vol Max n-day inflow volume (stored as an attribute of each + dataframe) + + dt Hydrograph timestep (delta time, dt) in hours (stored as an + attribute of each dataframe) + + The returned list also has a ‘probs’ attribute containing the + normalized sampling probabilities derived from ‘weights’. + +_E_x_a_m_p_l_e_s: + + # Setup with equal weights (default) + hydros <- hydrograph_setup(jmd_hydro_apr1999, + jmd_hydro_jun1965, + jmd_hydro_pmf, + critical_duration = 2, + routing_days = 10) + + # Setup with custom weights (PMF 3x more likely to be sampled) + hydros <- hydrograph_setup(jmd_hydro_apr1999, + jmd_hydro_jun1965, + jmd_hydro_pmf, + critical_duration = 2, + routing_days = 10, + weights = c(1, 1, 3)) + + # view normalized probabilities + attr(hydros, "probs") + +``` + +### `scale_hydrograph()` + +``` +_S_c_a_l_e _H_y_d_r_o_g_r_a_p_h + +_D_e_s_c_r_i_p_t_i_o_n: + + Scales inflow hydrograph given a sampled inflow volume from the + volume-frequency curve. Scale factor is defined by the sampled + inflow volume and the maximum volume of the corresponding + duration. If the native hydrograph timestep differs from the + target routing timestep, resampling is applied prior to scaling: + finer-to-coarser uses block averaging; coarser-to-finer uses + linear interpolation. + +_U_s_a_g_e: + + scale_hydrograph( + hydrograph_shape, + observed_volume, + sampled_volume, + routing_dt = 1 + ) + +_A_r_g_u_m_e_n_t_s: + +hydrograph_shape: Data frame with two columns: time (hours) and inflow + (cfs). Must be in that order. + +observed_volume: Maximum n-day inflow volume from input hydrograph + shape. + +sampled_volume: Sampled inflow volume from volume-frequency curve. + +routing_dt: Target routing timestep in hours. Supported values are + ‘0.25’ (15-min), ‘1’ (1-hour, default), ‘6’ (6-hour), and + ‘24’ (24-hour). + +_V_a_l_u_e: + + A data frame with two columns: ‘time_hrs’ and ‘inflow_cfs’, at the + target routing timestep, with time starting at hour 0. + +_E_x_a_m_p_l_e_s: + + # Example hydrograph. Requires pre-processing + hydro_example <- hydrograph_setup(jmd_hydro_jun1965_15min, critical_duration = 2, routing_days = 10) + hydrograph_shape <- hydro_example[[1]][, 2:3] + + # Default 1-hour routing timestep + scaled <- scale_hydrograph(hydrograph_shape, + observed_volume = 50000, + sampled_volume = 55000) + + # 15-min input hydrograph, 1-hour routing timestep + scaled <- scale_hydrograph(hydrograph_shape, + observed_volume = 50000, + sampled_volume = 55000, + routing_dt = 1) + +``` + +### `mod_puls_routing()` + +``` +_M_o_d_i_f_i_e_d _P_u_l_s _R_e_s_e_r_v_o_i_r _R_o_u_t_i_n_g + +_D_e_s_c_r_i_p_t_i_o_n: + + Performs Modified Puls (level pool) routing of inflow hydrograph + given a defined reservoir geometry (Stage (ft), Storage (ac-ft), + Discharge (cfs)). + +_U_s_a_g_e: + + mod_puls_routing(resmodel_df, inflow_df, initial_elev, full_results = FALSE) + +_A_r_g_u_m_e_n_t_s: + +resmodel_df: Data frame with three columns: elevation/stage (ft), + storage (acre-feet), and discharge (cfs). Must be in that + order. + +inflow_df: Data frame with two columns: time (hours) and inflow (cfs). + Must be in that order. + +initial_elev: Starting water surface elevation in feet. + +full_results: Logical. If ‘FALSE’ (default), returns only peak stage + and discharge. If ‘TRUE’, returns the complete routing + result. + +_V_a_l_u_e: + + If ‘full_results = FALSE’, a named numeric vector with + ‘peak_stage_ft’ and ‘peak_discharge_cfs’. If ‘full_results = + TRUE’, a data frame with columns: ‘time_hr’, ‘inflow_cfs’, + ‘elevation_ft’, ‘storage_acft’, and ‘outflow_cfs’. + +_R_e_f_e_r_e_n_c_e_s: + + Chow, V.T. (1959). Open-Channel Hydraulics. McGraw-Hill. + +_E_x_a_m_p_l_e_s: + + # Example hydrograph. Requires pre-processing + hydro_example <- hydrograph_setup(jmd_hydro_jun1965_15min, + critical_duration = 2, + routing_days = 10) + hydrograph_shape <- hydro_example[[1]][, 2:3] + scaled_hydrograph <- scale_hydrograph(hydrograph_shape, + observed_volume = 50000, + sampled_volume = 55000) + + # Peak values only + mod_puls_routing(jmd_resmodel, scaled_hydrograph, initial_elev = 3830) + + # Full routing table + jmd_full_routing <- mod_puls_routing(jmd_resmodel, + scaled_hydrograph, + initial_elev = 3830, + full_results = TRUE) + head(jmd_full_routing) + +``` + +### `stage_frequency_curve()` + +``` +_C_o_m_p_u_t_e _S_t_a_g_e-_F_r_e_q_u_e_n_c_y _C_u_r_v_e + +_D_e_s_c_r_i_p_t_i_o_n: + + Converts a matrix of routed peak stages into a stage-frequency + curve using the law of total probability across stratified bins. + For each candidate stage, the conditional exceedance probability + within each bin is weighted by the bin probability and summed to + produce the unconditional annual exceedance probability (AEP). + +_U_s_a_g_e: + + stage_frequency_curve(peakStage, weights, stage_bins = 1000) + +_A_r_g_u_m_e_n_t_s: + +peakStage: Matrix of peak stages [Mevents x Nbins] from routing. + + weights: Numeric vector of bin weights from ‘stratified_sampler()’. + Must have length equal to ‘ncol(peakStage)’. + +stage_bins: Numeric value of exceedance stages used to compute + stage-frequency in each bin. Default is 1000. + +_V_a_l_u_e: + + A data frame with columns: + + stage Evaluated stage values + + AEP Annual exceedance probability at each stage + +_S_e_e _A_l_s_o: + + ‘stratified_sampler()’, ‘flow_frequency_sampler()’, + ‘rfa_simulate()’ + +``` + +## Rejection Sampling + +### `pmf_stage_lognormal()` + +``` +_P_a_r_a_m_e_t_e_r_i_z_e _a _T_h_r_e_e-_P_a_r_a_m_e_t_e_r _L_o_g_n_o_r_m_a_l _P_M_F _f_o_r _S_t_a_g_e + +_D_e_s_c_r_i_p_t_i_o_n: + + Computes the parameters of a three-parameter lognormal + distribution for use as a probabilistic maximum stage (PMF) in + rejection sampling. The shift parameter defines the lower or upper + bound of the distribution (typically, the lower value). The + function supports two modes: (1) supplying a best estimate and + assuming sigma, or (2) supplying a best estimate, low, and high to + solve for sigma numerically. + +_U_s_a_g_e: + + pmf_stage_lognormal( + pmf_shift, + pmf_mean, + pmf_sigma = NULL, + pmf_low = NULL, + pmf_high = NULL + ) + +_A_r_g_u_m_e_n_t_s: + +pmf_shift: Numeric. Assumed lower-bound of the lognormal distribution. + This will define the shift value. Must be less than all of + ‘pmf_mean’, ‘pmf_low’, and ‘pmf_high’. + +pmf_mean: Numeric. Assumed mean of the shifted PMF stage distribution. + +pmf_sigma: Numeric. Assumed standard deviation on the log scale. + Required if ‘pmf_low’ and ‘pmf_high’ are not supplied. + Defaults to ‘NULL’. Suggested value = 0.5. + + pmf_low: Numeric. Low estimate of PMF stage (assumed 5th percentile). + Optional. If supplied, ‘pmf_high’ must also be supplied. + +pmf_high: Numeric. High estimate of PMF stage (assumed 95th + percentile). Optional. If supplied, ‘pmf_low’ must also be + supplied. + +_V_a_l_u_e: + + A named list with the following elements: + + pmf_shift Hard lower bound of the distribution. + + pmf_mean Assumed mean of shifted PMF stage distribution. + + pmf_sigma Standard deviation on the log scale. + + pmf_mu Derived location parameter on the log scale. + + pmf_p05 5th percentile of the PMF stage distribution. + + pmf_p95 95th percentile of the PMF stage distribution. + +_E_x_a_m_p_l_e_s: + + # Mode 1: assume sigma + pmf <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, pmf_sigma = 0.5) + + # Mode 2: solve for sigma from low/high estimates + pmf <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, + pmf_low = 239, pmf_high = 245) + +``` + +### `rejection_sampling_stage()` + +``` +_R_e_j_e_c_t_i_o_n _S_a_m_p_l_i_n_g _o_f _S_t_a_g_e _B_o_u_n_d_e_d _b_y _a _P_r_o_b_a_b_i_l_i_s_t_i_c _M_a_x_i_m_u_m _S_t_a_g_e + +_D_e_s_c_r_i_p_t_i_o_n: + + Draws ‘n_samples’ stage values from a stage-frequency curve, + rejecting any draws that exceed a probabilistic maximum stage + (PMF) sampled from a three-parameter lognormal distribution. + Accepted samples are ranked and assigned Weibull plotting + positions for use in frequency analysis. + +_U_s_a_g_e: + + rejection_sampling_stage( + pmf_stage_LN, + stage_freq_df = NULL, + aep = NULL, + stage = NULL, + n_samples = 1e+07 + ) + +_A_r_g_u_m_e_n_t_s: + +pmf_stage_LN: A named list returned by ‘pmf_stage_lognormal’ containing + the lognormal PMF parameters. + +stage_freq_df: A data frame with AEP in column 1 and stage in column 2. + Mutually exclusive with ‘aep’ and ‘stage’. + + aep: Numeric vector of annual exceedance probabilities. Must be + supplied with ‘stage’. Mutually exclusive with + ‘stage_freq_df’. + + stage: Numeric vector of stages corresponding to ‘aep’. Must be + supplied with ‘aep’. Mutually exclusive with ‘stage_freq_df’. + +n_samples: Integer. Number of samples to draw. Defaults to ‘1e7’. + +_V_a_l_u_e: + + A data frame of thinned accepted samples with columns for z-score + and stage, suitable for plotting a probabilistically bounded + stage-frequency curve. Output is produced by ‘thin_samples()’. + +_S_e_e _A_l_s_o: + + ‘pmf_stage_lognormal’ + +_E_x_a_m_p_l_e_s: + + ## Not run: + + pmf_ln <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, pmf_sigma = 0.5) + + result <- rejection_sampling_stage( + pmf_stage_LN = pmf_ln, + stage_freq_df = my_stage_freq_df, + n_samples = 1e7 + ) + ## End(Not run) + +``` + +## Additional Utilities + +### `bootstrap_vfc()` + +``` +_B_o_o_t_s_t_r_a_p _o_p_t_i_o_n _f_o_r _V_F_C (_R_F_A-_s_t_y_l_e _w/ _E_R_L) + +_D_e_s_c_r_i_p_t_i_o_n: + + Generates matrix of parameters using posterior mode/mean and ERL + +_U_s_a_g_e: + + bootstrap_vfc(bestfit_postmode, dist = "LP3", ERL = 150, Nboots = 10000) + +_A_r_g_u_m_e_n_t_s: + +bestfit_postmode: Vector of distribution parameters from RMC-BestFit. + For LP3: columns are mean (log), sd (log), skew (log). For + GEV: columns are location, scale, shape. + + dist: Distribution type. Either ‘"LP3"’ (default) or ‘"GEV"’. + + ERL: psuedo effective record length for bootstrapping + + Nboots: number of bootstraps + +_V_a_l_u_e: + + A list containing: + + params Matrix of bootstrapped parameters + + dist Distribution selected (LP3 or GEV) + + postmode Posterior mode parameters provided to bootstrap + + ERL pseudo effective record length provided to bootstrap + +_S_e_e _A_l_s_o: + + ‘flow_frequency_sampler()’ + +_E_x_a_m_p_l_e_s: + + # Sample using JMD VFC parameters + jmd_samples <- bootstrap_vfc(c(jmd_vfc_parameters$mean_log, + jmd_vfc_parameters$sd_log, + jmd_vfc_parameters$skew_log), + dist = "LP3", + ERL = jmd_vfc_parameters$erl) + + # GEV Example with parent distribution parameters + gev_example <- c(3.0, 1.0, -0.1) + gev_samples <- bootstrap_vfc(gev_example, + dist = "GEV", + ERL = 200, + Nboots = 5000) + hist(gev_samples$params[,1]) + + # LP3 Example with parent distribution parameters + lp3_example <- c(3.5, 0.22, 0.1) + lp3_samples <- bootstrap_vfc(lp3_example, + dist = "LP3", + ERL = 300, + Nboots = 1000) + hist(lp3_samples$params[,3]) + +``` + +### `qp3()` + +``` +_P_e_a_r_s_o_n _T_y_p_e _I_I_I _I_n_v_e_r_s_e _C_D_F (_Q_u_a_n_t_i_l_e _F_u_n_c_t_i_o_n) + +_D_e_s_c_r_i_p_t_i_o_n: + + Computes quantiles from a Pearson Type III distribution given + probabilities and distribution parameters (mean, standard + deviation, skewness). + +_U_s_a_g_e: + + qp3(p, mu, sigma, gamma) + +_A_r_g_u_m_e_n_t_s: + + p: Vector of probabilities (between 0 and 1). + + mu: Mean of the distribution. + + sigma: Standard deviation of the distribution. + + gamma: Skewness coefficient of the distribution. + +_D_e_t_a_i_l_s: + + NOTE: This is the non-vectorized version of the function (it will + be used in a loop) Vectorized version is part of future + development. + + The Pearson Type III distribution is parameterized by mean (‘mu’), + standard deviation (‘sigma’), and skewness (‘gamma’). These are + converted internally to location, scale, and shape parameters. + + When skewness is near zero (‘abs(gamma) < 1E-3’), the normal + distribution is used as an approximation. + +_V_a_l_u_e: + + Vector of quantiles corresponding to the input probabilities. + +_E_x_a_m_p_l_e_s: + + # Single quantile + qp3(0.99, mu = 10, sigma = 2, gamma = 0.5) + + # Multiple quantiles + qp3(c(0.5, 0.9, 0.99), mu = 10, sigma = 2, gamma = 0.5) + + # Use with JMD volume-frequency parameters + qp3(0.99, + mu = jmd_vfc_parameters$mean_log, + sigma = jmd_vfc_parameters$sd_log, + gamma = jmd_vfc_parameters$skew_log) + +``` + +### `aep2stage()` + +``` +_I_n_t_e_r_p_o_l_a_t_e _S_t_a_g_e _f_r_o_m _A_E_P + +_D_e_s_c_r_i_p_t_i_o_n: + + Given a stage-frequency curve, interpolates stage values at target + AEPs. Interpolation is performed in standard normal (z-variate) + space for better behavior across orders of magnitude in AEP. + +_U_s_a_g_e: + + aep2stage(aep, stage, interp_aep) + +_A_r_g_u_m_e_n_t_s: + + aep: Numeric vector of annual exceedance probabilities from the + known curve. + + stage: Numeric vector of stage values corresponding to ‘aep’. + +interp_aep: Numeric vector of target AEPs to interpolate at. + +_V_a_l_u_e: + + Numeric vector of interpolated stage values (same length as + ‘interp_aep’). + +_S_e_e _A_l_s_o: + + ‘stage2aep’ for the inverse operation. + +_E_x_a_m_p_l_e_s: + + # Get stage at the 1.12% and 0.175% AEP + aep2stage(jmd_rfa_expected$AEP, jmd_rfa_expected$Expected, c(0.0112,1.75E-3)) + +``` + +### `stage2aep()` + +``` +_I_n_t_e_r_p_o_l_a_t_e _A_E_P _f_r_o_m _S_t_a_g_e + +_D_e_s_c_r_i_p_t_i_o_n: + + Given a stage-frequency curve, interpolates AEP values at target + stages. Interpolation is performed in standard normal (z-variate) + space for better behavior across orders of magnitude in AEP. + +_U_s_a_g_e: + + stage2aep(aep, stage, interp_stage) + +_A_r_g_u_m_e_n_t_s: + + aep: Numeric vector of annual exceedance probabilities from the + known curve. + + stage: Numeric vector of stage values corresponding to ‘aep’. + +interp_stage: Numeric vector of target stage values to interpolate at. + +_D_e_t_a_i_l_s: + + Stage-frequency curves typically flatten at very rare AEPs once + the reservoir reaches the reservoir model discharge capacity. This + produces tied stage values. ‘approx()’ handles these by averaging + the corresponding AEPs, which may emit a "collapsing to unique 'x' + values" warning. This is expected behavior. + +_V_a_l_u_e: + + Numeric vector of interpolated AEP values (same length as + ‘interp_stage’). + +_S_e_e _A_l_s_o: + + ‘aep2stage’ for the inverse operation. + +_E_x_a_m_p_l_e_s: + + # Get AEP at stage 3862.0-ft and 3872-ft + stage2aep(jmd_rfa_expected$AEP, jmd_rfa_expected$Expected, c(3862.0, 3872)) + +``` + +### `interpolate_aep_matrix()` + +``` +_I_n_t_e_r_p_o_l_a_t_e _A_E_P_s _a_t _T_a_r_g_e_t _S_t_a_g_e_s _A_c_r_o_s_s _R_e_a_l_i_z_a_t_i_o_n_s + +_D_e_s_c_r_i_p_t_i_o_n: + + For each realization's stage-frequency curve, interpolates the AEP + at every target stage value. Interpolation is performed in + standard normal (z-variate) space. + +_U_s_a_g_e: + + interpolate_aep_matrix(results_list, target_stages) + +_A_r_g_u_m_e_n_t_s: + +results_list: List of realization data frames, each with columns + ‘stage’ and ‘AEP’. + +target_stages: Numeric vector of stage values to interpolate at. + +_V_a_l_u_e: + + Matrix of AEP values with rows = target stages and columns = + realizations. + +``` + +### `interpolate_stage_matrix()` + +``` +_I_n_t_e_r_p_o_l_a_t_e _S_t_a_g_e_s _a_t _T_a_r_g_e_t _A_E_P_s _A_c_r_o_s_s _R_e_a_l_i_z_a_t_i_o_n_s + +_D_e_s_c_r_i_p_t_i_o_n: + + For each realization's stage-frequency curve, interpolates the + stage at every target AEP value. Interpolation is performed in + standard normal (z-variate) space. + +_U_s_a_g_e: + + interpolate_stage_matrix(results_list, target_aeps) + +_A_r_g_u_m_e_n_t_s: + +results_list: List of realization data frames, each with columns + ‘stage’ and ‘AEP’. + +target_aeps: Numeric vector of AEP values to interpolate at. + +_V_a_l_u_e: + + Matrix of stage values with rows = target AEPs and columns = + realizations. + +``` + +### `power_function()` + +``` +_P_o_w_e_r _F_u_n_c_t_i_o_n + +_D_e_s_c_r_i_p_t_i_o_n: + + Raises 10 to the 1st through the nth power + +_U_s_a_g_e: + + power_function(x) + +_A_r_g_u_m_e_n_t_s: + + x: Highest power of 10 to use + +_V_a_l_u_e: + + Vector of 10 raised sequentially from the 1st to the nth power. + +``` + +### `theme_rfar_conceptual()` + +``` +_r_f_a_R _g_g_p_l_o_t_2 _T_h_e_m_e _f_o_r _C_o_n_c_e_p_t_u_a_l _P_l_o_t_s + +_D_e_s_c_r_i_p_t_i_o_n: + + A custom ‘ggplot2’ theme used in the rfaR conceptual realization + vignette. Based on ‘theme_bw’ with tightened text sizes, italic + titles, and the legend suppressed. Intended for figures that + emphasize curves over annotation density. + +_U_s_a_g_e: + + theme_rfar_conceptual() + +_V_a_l_u_e: + + A ‘ggplot2’ theme object that can be added to a ‘ggplot’ via ‘+’. + +_S_e_e _A_l_s_o: + + ‘theme_bw’ + +_E_x_a_m_p_l_e_s: + + library(ggplot2) + ggplot(jmd_rfa_expected, aes(x = AEP, y = Expected)) + + geom_line() + + scale_x_continuous(transform = c("log10", "reverse")) + + labs(title = "JMD Expected Stage-Frequency", + subtitle = "Demonstrating theme_rfar_conceptual()", + x = "AEP", y = "Stage (ft)") + + theme_rfar_conceptual() + +``` + diff --git a/rfaR-book/.gitignore b/rfaR-book/.gitignore new file mode 100644 index 0000000..ad29309 --- /dev/null +++ b/rfaR-book/.gitignore @@ -0,0 +1,2 @@ +/.quarto/ +**/*.quarto_ipynb diff --git a/rfaR-book/01-introduction.qmd b/rfaR-book/01-introduction.qmd new file mode 100644 index 0000000..fd10145 --- /dev/null +++ b/rfaR-book/01-introduction.qmd @@ -0,0 +1,46 @@ +# Introduction + +## Purpose + +`rfaR` is an R implementation of the Risk Management Center Reservoir +Frequency Analysis (RMC-RFA) software. RMC-RFA (and `rfaR`) use apply stratified +monte carlo methods to deterministic flood routing to produce a hydrologic hazard +curve[^1] at individual dam and levee sites. Hazard analyses support risk assessments +within the USACE Dam & Levee Safety Program. + +[^1]: In risk analysis, the *hazard* is the risk source where potential consequences relate to harm [@aven_society_nodate]. In the context of dam and levee risk analyses, the source of risk is often defined using a reservoir stage-frequency relationship. The terms *hydrologic loading*, *stage-frequency*, and *hydrologic hazard* are used interchangeably to relate reservoir/river stages to annual exceedance probabilities (AEPs). These relationships are commonly presented as curves and are used as probabilistic loading conditions in dam and levee risk analyses. + + +## rfaR Analysis Summary + +`rfaR` combines deterministic flood routing (Modified Puls) with a +nested, two-loop Monte Carlo framework to quantify: + +- **Natural variability** — simulated in the inner loop as a *realization*. Each + realization represents 10,000 indiviual flood events with randomly sampled + starting stage, hydrograph shape, and inflow volume (from an associated frequency distribution). + +- **Knowledge uncertainty (epistemic uncertainty)** — simulated in the outer loop, by sampling + different parameter sets (from LP3 or GEV flood frequency distributions, + informed by MCMC output from RMC-BestFit). + +## Relationship to RMC-RFA + +`rfaR` reimplements the core RMC-RFA methodology in open-source R, offering: + +- Reproducibility and transparency in the routing and sampling logic +- Integration with the broader R/RMC ecosystem (e.g., RMC-BestFit outputs) + +## How This Manual Is Organized + +- **Methodology** — statistical and hydrologic methods behind the two-loop + simulation framework +- **Installation** — getting `rfaR` running locally +- **Workflow** — an end-to-end walkthrough from raw inputs to a + stage-frequency curve +- **Simulation Modes** — Median, Expected, and Full Uncertainty simulation modes explained +- **Function Reference** — Function references (auto-generated from package documentation) +- **Validation** — comparison against RMC-RFA / HEC-HMS benchmark results + +For function-level documentation, also see the +[pkgdown reference site](https://usace-rmc.github.io/rfaR/). diff --git a/rfaR-book/02-methodology.qmd b/rfaR-book/02-methodology.qmd new file mode 100644 index 0000000..9efaab4 --- /dev/null +++ b/rfaR-book/02-methodology.qmd @@ -0,0 +1,17 @@ +# Methodology + +## Overview + +`rfaR` implements a two-loop, nested Monte Carlo framework to propagate both +natural variability and knowledge uncertainty through a deterministic flood +routing model. + +## Outer Loop: Parameter Uncertainty + + + +## Inner Loop: Realizations + + + +## Flood Routing diff --git a/rfaR-book/03-installation.qmd b/rfaR-book/03-installation.qmd new file mode 100644 index 0000000..89b3542 --- /dev/null +++ b/rfaR-book/03-installation.qmd @@ -0,0 +1,27 @@ +# Installation + +## Requirements + +- R (version ≥ *TODO*) +- Recommended: [Positron](https://positron.posit.co/) or RStudio + +## Installing rfaR + +```r +# From GitHub +remotes::install_github("USACE-RMC/rfaR") +``` + +*TODO: note CRAN install instructions once published.* + +## Dependencies + +*TODO: list key dependencies (ggplot2, future/future.apply, lmom, cli, etc.) +and any that require special handling on USACE machines.* + +## Verifying Installation + +```r +library(rfaR) +# TODO: smoke-test +``` diff --git a/rfaR-book/04-workflow.qmd b/rfaR-book/04-workflow.qmd new file mode 100644 index 0000000..02c68f2 --- /dev/null +++ b/rfaR-book/04-workflow.qmd @@ -0,0 +1,22 @@ +# Workflow + +## Overview + + +## Step 1: Prepare Inputs + + +## Step 2: Hydrograph Setup + + +## Step 3: Sampling + + + +## Step 4: Routing + + +## Step 5: Running the Full Simulation + + +## Step 6: Interpreting Results diff --git a/rfaR-book/05-function-reference.qmd b/rfaR-book/05-function-reference.qmd new file mode 100644 index 0000000..a43ea44 --- /dev/null +++ b/rfaR-book/05-function-reference.qmd @@ -0,0 +1,1224 @@ +# Function Reference + +## Execute Reservoir Frequency Analysis + +### `rfa_simulate()` + +``` +RFA Stage-Frequency Simulation + +Description: + + Performs reservoir stage frequency analysis using Monte Carlo + simulation to develop stage-frequency relationships. Combines + stratified flow-frequency sampling, flood seasonality, hydrograph + scaling, and Modified Puls reservoir routing to estimate annual + exceedance probabilities of reservoir stage. + +Usage: + + rfa_simulate( + sim_type = "expected", + bestfit_params, + dist = "LP3", + stage_ts, + seasonality, + hydrographs, + resmodel, + Nbins = 50, + events_per_bin = 200, + routing_dt = 1, + Ncores = NULL, + sim_name = NULL, + results_dir = NULL + ) + +Arguments: + +sim_type: Character string specifying the simulation mode. One of + ‘"median"’, ‘"expected"’, or ‘"full"’. Default is + ‘"expected"’. + +bestfit_params: Data frame or matrix of distribution parameters from + RMC-BestFit MCMC output. Columns 1-3 are distribution + parameters: + + * LP3: mean (log), standard deviation (log), skew (log) + + * GEV: location, scale, shape + + Column 4 is log-likelihood (used only in ‘"median"’ mode to + identify the posterior mode). For ‘"expected"’ mode, must + have at least ‘Nbins * events_per_bin’ rows. For ‘"full"’ + mode, each row is treated as an independent realization. + + dist: Character string specifying the frequency distribution. + Either ‘"LP3"’ (Log-Pearson Type III, default) or ‘"GEV"’ + (Generalized Extreme Value). + +stage_ts: Data frame of historical reservoir stage with columns ‘date’ + (character in M/D/YYYY format) and ‘stage’ (numeric, feet). + +seasonality: Numeric vector of length 12 giving monthly flood + occurrence probabilities (relative frequencies). Used to + sample the month of each simulated event, which determines + the antecedent pool elevation. + +hydrographs: List of hydrograph data frames as returned by + ‘hydrograph_setup’. Each element has columns ‘datetime’, + ‘hour’, ‘inflow’, and ‘hydrograph_num’, with attributes + ‘"obs_vol"’ (observed max n-day volume) and ‘"dt"’ (timestep + in hours). The list must have a ‘"probs"’ attribute + containing normalized sampling probabilities. + +resmodel: Data frame with three columns: elevation (ft), storage + (acre-ft), and discharge (cfs). Defines the reservoir model + for Modified Puls routing. + + Nbins: Integer. Number of stratified sampling bins in EV1 space. + Default is 50. + +events_per_bin: Integer. Number of events sampled per bin. Default is + 200. Total simulations per realization = ‘Nbins * + events_per_bin’. + +routing_dt: Numeric. Routing timestep in hours. Passed to + ‘scale_hydrograph’ to resample hydrographs before routing. + Supported values are ‘0.25’ (15-min), ‘1’ (1-hour, default), + ‘6’ (6-hour), and ‘24’ (24-hour). + + Ncores: Integer or ‘NULL’. Number of parallel workers for ‘"full"’ + mode. If ‘NULL’ (default), automatically selects based on + available cores (capped at 16). Ignored for ‘"median"’ and + ‘"expected"’ modes. + +sim_name: Optional character string to label the simulation. Used in + the output CSV filename. If ‘NULL’ (default), defaults to + ‘"sim"’. Spaces are replaced with underscores in the + filename. + +results_dir: Optional path to the directory where results are saved. If + ‘NULL’ (default), creates an ‘rfaR_results’ folder in the + current working directory. + +Details: + + Three simulation modes are available: + + ‘"median"’ Single realization using the most likely (posterior + mode) parameter set from RMC-BestFit. Produces one + stage-frequency curve with no uncertainty bounds. Useful for + quick screening or debugging. + + ‘"expected"’ Single realization using expected value sampling, + where each stratified flow sample is paired 1-to-1 with a + different parameter set from the posterior distribution. + Produces one stage-frequency curve representing the expected + (mean) result integrated across parameter uncertainty. + + ‘"full"’ Nested Monte Carlo with full uncertainty quantification. + The outer loop iterates over all parameter sets (one per row + of ‘bestfit_params’), and each inner loop independently + samples natural variability via stratified sampling, + seasonality, starting pool, and hydrograph shape. Each + realization produces an independent stage-frequency curve; + curves are combined on a common stage grid to yield expected, + median, and 5th/95th percentile confidence bounds. The outer + loop is parallelized using the ‘future’ framework. + + For all modes, natural variability is captured through stratified + sampling in with ‘Nbins’ bins and ‘events_per_bin’ events per bin, + providing reliable coverage from common events down to + approximately 1e-8 AEP. + + The stratified sampling approach divides the AEP range (default + 0.99 to 1e-8) into equal-width bins in EV1 (Gumbel reduced + variate) space and samples events uniformly within each bin. This + ensures adequate representation of rare flood events that would + require orders of magnitude more samples under crude Monte Carlo + sampling. + + Post-processing uses the law of total probability via + ‘stage_frequency_curve’: for a grid of stage thresholds, the + weighted exceedance fraction is computed within each bin and + summed across bins. + + In ‘"full"’ mode, each parallel worker independently samples all + random inputs (seasonality, starting pool, hydrograph shape, and + stratified z-variates) in addition to using its own parameter set. + This ensures full Monte Carlo independence across realizations. + Each worker post-processes its results into a stage-frequency + curve immediately, returning only the curve rather than the full + peak stage matrix, keeping memory usage manageable for large + numbers of realizations. + + Curves from individual realizations are combined by interpolating + each onto a common stage grid (spanning the global min/max across + all realizations) in log10(AEP) space, then computing summary + statistics across realizations at each stage threshold. + + Results are automatically exported as a CSV file named + ‘{sim_name}_{sim_type}_{MM_DD_YY_HHMM}.csv’ in the ‘results_dir’ + directory. For example, a median simulation named "John McGraw + Dam" run on June 4, 2025 at 2:30 PM would produce + ‘John_McGraw_Dam_median_06_04_25_1430.csv’. + +Value: + + A list whose contents depend on ‘sim_type’: + + For ‘"median"’ and ‘"expected"’: + + stage_frequency Data frame with columns ‘stage’ and ‘AEP’ from + ‘stage_frequency_curve’. + + peakStage Matrix of simulated peak stages (‘events_per_bin’ rows + by ‘Nbins’ columns). + + peakFlow Matrix of simulated peak discharges (same dimensions). + + weights Stratified sampling bin weights. + + For ‘"full"’: + + stage_frequency Data frame with columns ‘stage’, ‘expected’, + ‘median’, ‘lower_05’, and ‘upper_95’ on a common stage grid. + + all_curves List of per-realization stage-frequency data frames + (each with ‘stage’ and ‘AEP’ columns). + + aep_matrix Matrix of interpolated AEP values (500 rows by + ‘Nrealizations’ columns) on the common stage grid. + + common_stage Numeric vector of the common stage grid (length 500). + + Nrealizations Number of parameter set realizations processed. + + Nsims_per_realiz Number of simulations per realization. + +See Also: + + ‘flow_frequency_sampler’, ‘flow_frequency_sampler_expected’, + ‘stratified_sampler’, ‘scale_hydrograph’, ‘mod_puls_routing’, + ‘stage_frequency_curve’, ‘hydrograph_setup’ + +Examples: + + ## Not run: + + # Not run: Expected and Median modes take ~15s; Full uncert + # runs ~10,000 parameter realizations and takes 3-4 hours on a + # typical machine. + + # --- Setup --- + hydros <- hydrograph_setup(jmd_hydro_apr1999, jmd_hydro_jun1965, + jmd_hydro_may1955, jmd_hydro_pmf, + critical_duration = 2, routing_days = 10) + + # --- Expected only (default) --- + results_exp <- rfa_simulate( + sim_type = "expected", + bestfit_params = jmd_bf_parameter_sets, + stage_ts = jmd_wy1980_stage, + seasonality = jmd_seasonality$relative_frequency, + hydrographs = hydros, + resmodel = jmd_resmodel, + sim_name = "jmd" + ) + + # --- Median only --- + results_med <- rfa_simulate( + sim_type = "median", + bestfit_params = jmd_bf_parameter_sets, + stage_ts = jmd_wy1980_stage, + seasonality = jmd_seasonality$relative_frequency, + hydrographs = hydros, + resmodel = jmd_resmodel, + sim_name = "jmd" + ) + + # --- Full uncertainty (parallelized) --- + results_full <- rfa_simulate( + sim_type = "full", + bestfit_params = jmd_bf_parameter_sets, + stage_ts = jmd_wy1980_stage, + seasonality = jmd_seasonality$relative_frequency, + hydrographs = hydros, + resmodel = jmd_resmodel, + Ncores = 4, + sim_name = "jmd" + ) + ## End(Not run) + +``` + +## Primary RFA Modules + +### `stratified_sampler()` + +``` +Stratified Sampler + +Description: + + Creates stratified bins in standard normal space for use in + stratified sampling. Stratification improves sampling efficiency + by ensuring adequate coverage of rare events. + +Usage: + + stratified_sampler( + minAEP = 1e-08, + maxAEP = 0.99, + dist = "EV1", + Nbins = NULL, + Mevents = NULL, + verbose = FALSE + ) + +Arguments: + + minAEP: Minimum annual exceedance probability. Default is ‘1E-8’. + + maxAEP: Maximum annual exceedance probability. Default is ‘0.99’. + + dist: Character. Probability space for stratification. + Case-insensitive. Default is ‘"EV1"’. Invalid values trigger + a warning and default to ‘"EV1"’. + + ‘"EV1"’ Extreme Value Type I (Gumbel) space. Recommended for + flood frequency analysis. Allocates more bins to rare + events through the transformation ‘-log(-log(1-AEP))’, + improving tail estimation efficiency. + + ‘"Normal"’ Standard normal (z-score) space. Uniform bins in + z-space. Use when the underlying phenomenon is normally + distributed. + + ‘"Uniform"’ Uniform probability space. Equal probability + width per bin. Generally inefficient for rare event + estimation. + + Nbins: Number of stratified bins. Default is ‘20’. + + Mevents: Number of events per bin. Default is ‘500’. + + verbose: Logical. If ‘TRUE’, prints a completion message summarizing + the stratification. Default is ‘FALSE’. + +Details: + + The function divides the probability space into bins using the + specified transformation. EV1 transformation is recommended for + heavy-tailed distributions common in flood frequency analysis, as + it naturally allocates more sampling effort to rare events + critical for dam safety assessments. + +Value: + + A list containing: + + normOrd Vector of standard normal ordinates spanning all bins + + Weights Vector of probability weights for each bin + + Zlower Vector of lower bounds for each bin (standard normal) + + Zupper Vector of upper bounds for each bin (standard normal) + + Nbins Number of bins + + Mevents Number of events per bin + +Examples: + + # Default stratification + strat <- stratified_sampler() + + # Custom bins and events + strat <- stratified_sampler(minAEP = 1E-6, + maxAEP = 0.5, + dist = "EV1", + Nbins = 10, + Mevents = 100) + + # With verbose message + strat <- stratified_sampler(verbose = TRUE) + +``` + +### `flow_frequency_sampler()` + +``` +Flow Frequency Sampler + +Description: + + Generates a stratified matrix of flow values from a single set of + frequency distribution parameters using stratified Monte Carlo + sampling. Used internally by ‘rfa_simulate()’ for median-only and + full uncertainty modes. + +Usage: + + flow_frequency_sampler( + bestfit_params, + freq_dist = "LP3", + strat_dist = "ev1", + Nbin = NULL, + Mevent = NULL + ) + +Arguments: + +bestfit_params: Numeric vector of length 3 containing distribution + parameters. For LP3: ‘c(mean_log, sd_log, skew_log)’. For + GEV: ‘c(location, scale, shape)’. + +freq_dist: Character. Distribution type. Either ‘"LP3"’ (default) or + ‘"GEV"’. + +strat_dist: Character. Probability space for stratification bins. + Passed to ‘stratified_sampler()’. One of ‘"ev1"’ (default), + ‘"normal"’, or ‘"uniform"’. See ‘stratified_sampler()’ for + details. + + Nbin: Integer. Number of stratified bins. Default is ‘50’. + + Mevent: Integer. Number of events per bin. Default is ‘200’. + +Value: + + A list containing: + + flow Matrix of sampled flow values [Mevent x Nbin] + + nbins Number of stratified bins + + mevents Number of events per bin + + weights Probability weights for each bin from + ‘stratified_sampler()’ + +See Also: + + ‘stratified_sampler()’, ‘qp3()’, ‘rfa_simulate()’ + +Examples: + + # Single parameter set (posterior mode) + params <- c(4.85, 0.39, -0.15) + result <- flow_frequency_sampler(params, freq_dist = "LP3", + Nbin = 20, Mevent = 500) + # Dimensions of result + dim(result$flow) # 500 x 20 + + # Distribution of Sampled Flows + hist(result$flow) + +``` + +### `flow_frequency_sampler_expected()` + +``` +Flow Frequency Sampler (Expected Only) + +Description: + + Generates a stratified matrix of flow values by pairing each + z-ordinate with a different parameter set from the posterior + distribution. This collapses the nested Monte Carlo structure into + a single pass, simultaneously sampling natural variability (via + stratified z-ordinates) and knowledge uncertainty (via varying + parameters). Used internally by ‘rfa_simulate()’ for expected-only + mode. + +Usage: + + flow_frequency_sampler_expected( + bestfit_params, + freq_dist = "LP3", + strat_dist = "ev1", + Nbin = NULL, + Mevent = NULL + ) + +Arguments: + +bestfit_params: Data frame of distribution parameters from RMC-BestFit. + Must have ‘Nbin * Mevent’ rows (one parameter set per + z-ordinate). For LP3: columns are mean (log), sd (log), skew + (log). For GEV: columns are location, scale, shape. + +freq_dist: Character. Distribution type. Either ‘"LP3"’ (default) or + ‘"GEV"’. + +strat_dist: Character. Probability space for stratification bins. + Passed to ‘stratified_sampler()’. One of ‘"ev1"’ (default), + ‘"normal"’, or ‘"uniform"’. See ‘stratified_sampler()’ for + details. + + Nbin: Integer. Number of stratified bins. Default is ‘50’. + + Mevent: Integer. Number of events per bin. Default is ‘200’. + +Value: + + A list containing: + + flow Matrix of sampled flow values [Mevent x Nbin] + + nbins Number of stratified bins + + mevents Number of events per bin + + weights Probability weights for each bin from + ‘stratified_sampler()’ + +See Also: + + ‘stratified_sampler()’, ‘qp3()’, ‘flow_frequency_sampler()’, + ‘rfa_simulate()’ + +Examples: + + # Using a pre-loaded parameter set (all 10,000 parameter sets) + result <- flow_frequency_sampler_expected(jmd_bf_parameter_sets, + Nbin = 20, Mevent = 500) + dim(result$flow) # 500 x 20 + + # Using bootstrapped parameter samples + jmd_samples <- bootstrap_vfc( + c(jmd_vfc_parameters$mean_log, + jmd_vfc_parameters$sd_log, + jmd_vfc_parameters$skew_log), + dist = "LP3", + ERL = jmd_vfc_parameters$erl) + + jmd_result <- flow_frequency_sampler_expected( + jmd_samples$params, + freq_dist = "LP3", + Nbin = 20, + Mevent = 500) + +``` + +### `hydrograph_setup()` + +``` +Hydrograph Setup for RFA Simulation + +Description: + + Prepares hydrograph data frames copied from RMC-RFA for use in RFA + simulation. Converts date/time columns and adds sequential hour + and hydrograph ID columns. + +Usage: + + hydrograph_setup( + ..., + critical_duration = NULL, + routing_days = NULL, + weights = NULL + ) + +Arguments: + + ...: Data frame representing an input hydrograph with columns: + Ord, Date, Time, Flow (cfs). Copied directly from RMC-RFA. + Date & Time should be ‘class() = "character"’ + +critical_duration: Critical duration in days. + +routing_days: Desired length of routing simulation in days. + + weights: Optional numeric vector of sampling weights for each + hydrograph. Must be the same length as the number of input + hydrographs. Weights are normalized to probabilities + internally. If NULL (default), all hydrographs are weighted + equally. + +Value: + + A list of formatted hydrograph data frames, each containing: + + datetime Date-time (POSIXct) + + hour Hours from start of event + + inflow Inflow (cfs) + + hydrograph_num Hydrograph ID number for sampling + + obs_vol Max n-day inflow volume (stored as an attribute of each + dataframe) + + dt Hydrograph timestep (delta time, dt) in hours (stored as an + attribute of each dataframe) + + The returned list also has a ‘probs’ attribute containing the + normalized sampling probabilities derived from ‘weights’. + +Examples: + + # Setup with equal weights (default) + hydros <- hydrograph_setup(jmd_hydro_apr1999, + jmd_hydro_jun1965, + jmd_hydro_pmf, + critical_duration = 2, + routing_days = 10) + + # Setup with custom weights (PMF 3x more likely to be sampled) + hydros <- hydrograph_setup(jmd_hydro_apr1999, + jmd_hydro_jun1965, + jmd_hydro_pmf, + critical_duration = 2, + routing_days = 10, + weights = c(1, 1, 3)) + + # view normalized probabilities + attr(hydros, "probs") + +``` + +### `scale_hydrograph()` + +``` +Scale Hydrograph + +Description: + + Scales inflow hydrograph given a sampled inflow volume from the + volume-frequency curve. Scale factor is defined by the sampled + inflow volume and the maximum volume of the corresponding + duration. If the native hydrograph timestep differs from the + target routing timestep, resampling is applied prior to scaling: + finer-to-coarser uses block averaging; coarser-to-finer uses + linear interpolation. + +Usage: + + scale_hydrograph( + hydrograph_shape, + observed_volume, + sampled_volume, + routing_dt = 1 + ) + +Arguments: + +hydrograph_shape: Data frame with two columns: time (hours) and inflow + (cfs). Must be in that order. + +observed_volume: Maximum n-day inflow volume from input hydrograph + shape. + +sampled_volume: Sampled inflow volume from volume-frequency curve. + +routing_dt: Target routing timestep in hours. Supported values are + ‘0.25’ (15-min), ‘1’ (1-hour, default), ‘6’ (6-hour), and + ‘24’ (24-hour). + +Value: + + A data frame with two columns: ‘time_hrs’ and ‘inflow_cfs’, at the + target routing timestep, with time starting at hour 0. + +Examples: + + # Example hydrograph. Requires pre-processing + hydro_example <- hydrograph_setup(jmd_hydro_jun1965_15min, critical_duration = 2, routing_days = 10) + hydrograph_shape <- hydro_example[[1]][, 2:3] + + # Default 1-hour routing timestep + scaled <- scale_hydrograph(hydrograph_shape, + observed_volume = 50000, + sampled_volume = 55000) + + # 15-min input hydrograph, 1-hour routing timestep + scaled <- scale_hydrograph(hydrograph_shape, + observed_volume = 50000, + sampled_volume = 55000, + routing_dt = 1) + +``` + +### `mod_puls_routing()` + +``` +Modified Puls Reservoir Routing + +Description: + + Performs Modified Puls (level pool) routing of inflow hydrograph + given a defined reservoir geometry (Stage (ft), Storage (ac-ft), + Discharge (cfs)). + +Usage: + + mod_puls_routing(resmodel_df, inflow_df, initial_elev, full_results = FALSE) + +Arguments: + +resmodel_df: Data frame with three columns: elevation/stage (ft), + storage (acre-feet), and discharge (cfs). Must be in that + order. + +inflow_df: Data frame with two columns: time (hours) and inflow (cfs). + Must be in that order. + +initial_elev: Starting water surface elevation in feet. + +full_results: Logical. If ‘FALSE’ (default), returns only peak stage + and discharge. If ‘TRUE’, returns the complete routing + result. + +Value: + + If ‘full_results = FALSE’, a named numeric vector with + ‘peak_stage_ft’ and ‘peak_discharge_cfs’. If ‘full_results = + TRUE’, a data frame with columns: ‘time_hr’, ‘inflow_cfs’, + ‘elevation_ft’, ‘storage_acft’, and ‘outflow_cfs’. + +References: + + Chow, V.T. (1959). Open-Channel Hydraulics. McGraw-Hill. + +Examples: + + # Example hydrograph. Requires pre-processing + hydro_example <- hydrograph_setup(jmd_hydro_jun1965_15min, + critical_duration = 2, + routing_days = 10) + hydrograph_shape <- hydro_example[[1]][, 2:3] + scaled_hydrograph <- scale_hydrograph(hydrograph_shape, + observed_volume = 50000, + sampled_volume = 55000) + + # Peak values only + mod_puls_routing(jmd_resmodel, scaled_hydrograph, initial_elev = 3830) + + # Full routing table + jmd_full_routing <- mod_puls_routing(jmd_resmodel, + scaled_hydrograph, + initial_elev = 3830, + full_results = TRUE) + head(jmd_full_routing) + +``` + +### `stage_frequency_curve()` + +``` +Compute Stage-Frequency Curve + +Description: + + Converts a matrix of routed peak stages into a stage-frequency + curve using the law of total probability across stratified bins. + For each candidate stage, the conditional exceedance probability + within each bin is weighted by the bin probability and summed to + produce the unconditional annual exceedance probability (AEP). + +Usage: + + stage_frequency_curve(peakStage, weights, stage_bins = 1000) + +Arguments: + +peakStage: Matrix of peak stages [Mevents x Nbins] from routing. + + weights: Numeric vector of bin weights from ‘stratified_sampler()’. + Must have length equal to ‘ncol(peakStage)’. + +stage_bins: Numeric value of exceedance stages used to compute + stage-frequency in each bin. Default is 1000. + +Value: + + A data frame with columns: + + stage Evaluated stage values + + AEP Annual exceedance probability at each stage + +See Also: + + ‘stratified_sampler()’, ‘flow_frequency_sampler()’, + ‘rfa_simulate()’ + +``` + +## Rejection Sampling + +### `pmf_stage_lognormal()` + +``` +Parameterize a Three-Parameter Lognormal PMF for Stage + +Description: + + Computes the parameters of a three-parameter lognormal + distribution for use as a probabilistic maximum stage (PMF) in + rejection sampling. The shift parameter defines the lower or upper + bound of the distribution (typically, the lower value). The + function supports two modes: (1) supplying a best estimate and + assuming sigma, or (2) supplying a best estimate, low, and high to + solve for sigma numerically. + +Usage: + + pmf_stage_lognormal( + pmf_shift, + pmf_mean, + pmf_sigma = NULL, + pmf_low = NULL, + pmf_high = NULL + ) + +Arguments: + +pmf_shift: Numeric. Assumed lower-bound of the lognormal distribution. + This will define the shift value. Must be less than all of + ‘pmf_mean’, ‘pmf_low’, and ‘pmf_high’. + +pmf_mean: Numeric. Assumed mean of the shifted PMF stage distribution. + +pmf_sigma: Numeric. Assumed standard deviation on the log scale. + Required if ‘pmf_low’ and ‘pmf_high’ are not supplied. + Defaults to ‘NULL’. Suggested value = 0.5. + + pmf_low: Numeric. Low estimate of PMF stage (assumed 5th percentile). + Optional. If supplied, ‘pmf_high’ must also be supplied. + +pmf_high: Numeric. High estimate of PMF stage (assumed 95th + percentile). Optional. If supplied, ‘pmf_low’ must also be + supplied. + +Value: + + A named list with the following elements: + + pmf_shift Hard lower bound of the distribution. + + pmf_mean Assumed mean of shifted PMF stage distribution. + + pmf_sigma Standard deviation on the log scale. + + pmf_mu Derived location parameter on the log scale. + + pmf_p05 5th percentile of the PMF stage distribution. + + pmf_p95 95th percentile of the PMF stage distribution. + +Examples: + + # Mode 1: assume sigma + pmf <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, pmf_sigma = 0.5) + + # Mode 2: solve for sigma from low/high estimates + pmf <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, + pmf_low = 239, pmf_high = 245) + +``` + +### `rejection_sampling_stage()` + +``` +Rejection Sampling of Stage Bounded by a Probabilistic Maximum Stage + +Description: + + Draws ‘n_samples’ stage values from a stage-frequency curve, + rejecting any draws that exceed a probabilistic maximum stage + (PMF) sampled from a three-parameter lognormal distribution. + Accepted samples are ranked and assigned Weibull plotting + positions for use in frequency analysis. + +Usage: + + rejection_sampling_stage( + pmf_stage_LN, + stage_freq_df = NULL, + aep = NULL, + stage = NULL, + n_samples = 1e+07 + ) + +Arguments: + +pmf_stage_LN: A named list returned by ‘pmf_stage_lognormal’ containing + the lognormal PMF parameters. + +stage_freq_df: A data frame with AEP in column 1 and stage in column 2. + Mutually exclusive with ‘aep’ and ‘stage’. + + aep: Numeric vector of annual exceedance probabilities. Must be + supplied with ‘stage’. Mutually exclusive with + ‘stage_freq_df’. + + stage: Numeric vector of stages corresponding to ‘aep’. Must be + supplied with ‘aep’. Mutually exclusive with ‘stage_freq_df’. + +n_samples: Integer. Number of samples to draw. Defaults to ‘1e7’. + +Value: + + A data frame of thinned accepted samples with columns for z-score + and stage, suitable for plotting a probabilistically bounded + stage-frequency curve. Output is produced by ‘thin_samples()’. + +See Also: + + ‘pmf_stage_lognormal’ + +Examples: + + ## Not run: + + pmf_ln <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, pmf_sigma = 0.5) + + result <- rejection_sampling_stage( + pmf_stage_LN = pmf_ln, + stage_freq_df = my_stage_freq_df, + n_samples = 1e7 + ) + ## End(Not run) + +``` + +## Additional Utilities + +### `bootstrap_vfc()` + +``` +Bootstrap option for VFC (RFA-style w/ ERL) + +Description: + + Generates matrix of parameters using posterior mode/mean and ERL + +Usage: + + bootstrap_vfc(bestfit_postmode, dist = "LP3", ERL = 150, Nboots = 10000) + +Arguments: + +bestfit_postmode: Vector of distribution parameters from RMC-BestFit. + For LP3: columns are mean (log), sd (log), skew (log). For + GEV: columns are location, scale, shape. + + dist: Distribution type. Either ‘"LP3"’ (default) or ‘"GEV"’. + + ERL: psuedo effective record length for bootstrapping + + Nboots: number of bootstraps + +Value: + + A list containing: + + params Matrix of bootstrapped parameters + + dist Distribution selected (LP3 or GEV) + + postmode Posterior mode parameters provided to bootstrap + + ERL pseudo effective record length provided to bootstrap + +See Also: + + ‘flow_frequency_sampler()’ + +Examples: + + # Sample using JMD VFC parameters + jmd_samples <- bootstrap_vfc(c(jmd_vfc_parameters$mean_log, + jmd_vfc_parameters$sd_log, + jmd_vfc_parameters$skew_log), + dist = "LP3", + ERL = jmd_vfc_parameters$erl) + + # GEV Example with parent distribution parameters + gev_example <- c(3.0, 1.0, -0.1) + gev_samples <- bootstrap_vfc(gev_example, + dist = "GEV", + ERL = 200, + Nboots = 5000) + hist(gev_samples$params[,1]) + + # LP3 Example with parent distribution parameters + lp3_example <- c(3.5, 0.22, 0.1) + lp3_samples <- bootstrap_vfc(lp3_example, + dist = "LP3", + ERL = 300, + Nboots = 1000) + hist(lp3_samples$params[,3]) + +``` + +### `qp3()` + +``` +Pearson Type III Inverse CDF (Quantile Function) + +Description: + + Computes quantiles from a Pearson Type III distribution given + probabilities and distribution parameters (mean, standard + deviation, skewness). + +Usage: + + qp3(p, mu, sigma, gamma) + +Arguments: + + p: Vector of probabilities (between 0 and 1). + + mu: Mean of the distribution. + + sigma: Standard deviation of the distribution. + + gamma: Skewness coefficient of the distribution. + +Details: + + NOTE: This is the non-vectorized version of the function (it will + be used in a loop) Vectorized version is part of future + development. + + The Pearson Type III distribution is parameterized by mean (‘mu’), + standard deviation (‘sigma’), and skewness (‘gamma’). These are + converted internally to location, scale, and shape parameters. + + When skewness is near zero (‘abs(gamma) < 1E-3’), the normal + distribution is used as an approximation. + +Value: + + Vector of quantiles corresponding to the input probabilities. + +Examples: + + # Single quantile + qp3(0.99, mu = 10, sigma = 2, gamma = 0.5) + + # Multiple quantiles + qp3(c(0.5, 0.9, 0.99), mu = 10, sigma = 2, gamma = 0.5) + + # Use with JMD volume-frequency parameters + qp3(0.99, + mu = jmd_vfc_parameters$mean_log, + sigma = jmd_vfc_parameters$sd_log, + gamma = jmd_vfc_parameters$skew_log) + +``` + +### `aep2stage()` + +``` +Interpolate Stage from AEP + +Description: + + Given a stage-frequency curve, interpolates stage values at target + AEPs. Interpolation is performed in standard normal (z-variate) + space for better behavior across orders of magnitude in AEP. + +Usage: + + aep2stage(aep, stage, interp_aep) + +Arguments: + + aep: Numeric vector of annual exceedance probabilities from the + known curve. + + stage: Numeric vector of stage values corresponding to ‘aep’. + +interp_aep: Numeric vector of target AEPs to interpolate at. + +Value: + + Numeric vector of interpolated stage values (same length as + ‘interp_aep’). + +See Also: + + ‘stage2aep’ for the inverse operation. + +Examples: + + # Get stage at the 1.12% and 0.175% AEP + aep2stage(jmd_rfa_expected$AEP, jmd_rfa_expected$Expected, c(0.0112,1.75E-3)) + +``` + +### `stage2aep()` + +``` +Interpolate AEP from Stage + +Description: + + Given a stage-frequency curve, interpolates AEP values at target + stages. Interpolation is performed in standard normal (z-variate) + space for better behavior across orders of magnitude in AEP. + +Usage: + + stage2aep(aep, stage, interp_stage) + +Arguments: + + aep: Numeric vector of annual exceedance probabilities from the + known curve. + + stage: Numeric vector of stage values corresponding to ‘aep’. + +interp_stage: Numeric vector of target stage values to interpolate at. + +Details: + + Stage-frequency curves typically flatten at very rare AEPs once + the reservoir reaches the reservoir model discharge capacity. This + produces tied stage values. ‘approx()’ handles these by averaging + the corresponding AEPs, which may emit a "collapsing to unique 'x' + values" warning. This is expected behavior. + +Value: + + Numeric vector of interpolated AEP values (same length as + ‘interp_stage’). + +See Also: + + ‘aep2stage’ for the inverse operation. + +Examples: + + # Get AEP at stage 3862.0-ft and 3872-ft + stage2aep(jmd_rfa_expected$AEP, jmd_rfa_expected$Expected, c(3862.0, 3872)) + +``` + +### `interpolate_aep_matrix()` + +``` +Interpolate AEPs at Target Stages Across Realizations + +Description: + + For each realization's stage-frequency curve, interpolates the AEP + at every target stage value. Interpolation is performed in + standard normal (z-variate) space. + +Usage: + + interpolate_aep_matrix(results_list, target_stages) + +Arguments: + +results_list: List of realization data frames, each with columns + ‘stage’ and ‘AEP’. + +target_stages: Numeric vector of stage values to interpolate at. + +Value: + + Matrix of AEP values with rows = target stages and columns = + realizations. + +``` + +### `interpolate_stage_matrix()` + +``` +Interpolate Stages at Target AEPs Across Realizations + +Description: + + For each realization's stage-frequency curve, interpolates the + stage at every target AEP value. Interpolation is performed in + standard normal (z-variate) space. + +Usage: + + interpolate_stage_matrix(results_list, target_aeps) + +Arguments: + +results_list: List of realization data frames, each with columns + ‘stage’ and ‘AEP’. + +target_aeps: Numeric vector of AEP values to interpolate at. + +Value: + + Matrix of stage values with rows = target AEPs and columns = + realizations. + +``` + +### `power_function()` + +``` +Power Function + +Description: + + Raises 10 to the 1st through the nth power + +Usage: + + power_function(x) + +Arguments: + + x: Highest power of 10 to use + +Value: + + Vector of 10 raised sequentially from the 1st to the nth power. + +``` + +### `theme_rfar_conceptual()` + +``` +rfaR ggplot2 Theme for Conceptual Plots + +Description: + + A custom ‘ggplot2’ theme used in the rfaR conceptual realization + vignette. Based on ‘theme_bw’ with tightened text sizes, italic + titles, and the legend suppressed. Intended for figures that + emphasize curves over annotation density. + +Usage: + + theme_rfar_conceptual() + +Value: + + A ‘ggplot2’ theme object that can be added to a ‘ggplot’ via ‘+’. + +See Also: + + ‘theme_bw’ + +Examples: + + library(ggplot2) + ggplot(jmd_rfa_expected, aes(x = AEP, y = Expected)) + + geom_line() + + scale_x_continuous(transform = c("log10", "reverse")) + + labs(title = "JMD Expected Stage-Frequency", + subtitle = "Demonstrating theme_rfar_conceptual()", + x = "AEP", y = "Stage (ft)") + + theme_rfar_conceptual() + +``` + diff --git a/rfaR-book/06-simulation-modes.qmd b/rfaR-book/06-simulation-modes.qmd new file mode 100644 index 0000000..a0cb6ab --- /dev/null +++ b/rfaR-book/06-simulation-modes.qmd @@ -0,0 +1,18 @@ +# Simulation Modes + +`rfaR` supports three simulation modes, corresponding to different treatments +of parameter uncertainty. + +## Median Mode + + +## Expected Mode + + +## Full Uncertainty Mode + +Runs the full nested Monte Carlo framework, propagating both parameter +(epistemic) and event (aleatory) uncertainty. + + +## Selecting a Mode diff --git a/rfaR-book/07-validation.qmd b/rfaR-book/07-validation.qmd new file mode 100644 index 0000000..50e26fb --- /dev/null +++ b/rfaR-book/07-validation.qmd @@ -0,0 +1,15 @@ +# Validation + +## Purpose + + +## Modified Puls Routing + + +## Stratified Sampling + + +## See Also + +Detailed validation vignettes are also available on the +[pkgdown site](https://usace-rmc.github.io/rfaR/articles/index.html). diff --git a/rfaR-book/_book/01-introduction.html b/rfaR-book/_book/01-introduction.html new file mode 100644 index 0000000..72226b3 --- /dev/null +++ b/rfaR-book/_book/01-introduction.html @@ -0,0 +1,1015 @@ + + + + + + + + + +1  Introduction – rfaR: Reservoir Frequency Analysis in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ + +
+
+

1  Introduction

+
+ + + +
+ + + + +
+ + + +
+ + +
+

1.1 Purpose

+

rfaR is an R implementation of the Risk Management Center Reservoir Frequency Analysis (RMC-RFA) software. RMC-RFA (and rfaR) use apply stratified monte carlo methods to deterministic flood routing to produce a hydrologic hazard curve1 at individual dam and levee sites. Hazard analyses support risk assessments within the USACE Dam & Levee Safety Program.

+
+
+

1.2 rfaR Analysis Summary

+

rfaR combines deterministic flood routing (Modified Puls) with a nested, two-loop Monte Carlo framework to quantify:

+
    +

  • Natural variability — simulated in the inner loop as a realization. Each realization represents 10,000 indiviual flood events with randomly sampled starting stage, hydrograph shape, and inflow volume (from an associated frequency distribution).

    • +

  • Knowledge uncertainty (epistemic uncertainty) — simulated in the outer loop, by sampling different parameter sets (from LP3 or GEV flood frequency distributions, informed by MCMC output from RMC-BestFit).

    • +
+
+
+

1.3 Relationship to RMC-RFA

+

rfaR reimplements the core RMC-RFA methodology in open-source R, offering:

+
    +
  • Reproducibility and transparency in the routing and sampling logic
    • +
  • Integration with the broader R/RMC ecosystem (e.g., RMC-BestFit outputs)
    • +
+
+
+

1.4 How This Manual Is Organized

+
    +
  • Methodology — statistical and hydrologic methods behind the two-loop simulation framework
    • +
  • Installation — getting rfaR running locally
    • +
  • Workflow — an end-to-end walkthrough from raw inputs to a stage-frequency curve
    • +
  • Simulation Modes — Median, Expected, and Full Uncertainty simulation modes explained
    • +
  • Function Reference — Function references (auto-generated from package documentation)
    • +
  • Validation — comparison against RMC-RFA / HEC-HMS benchmark results
    • +
+

For function-level documentation, also see the pkgdown reference site.

+ + + + +
+
+Aven, Terje, Yakov Ben-Haim, Henning Boje Andersen, et al. n.d. Society for Risk Analysis Glossary. +
+
+
+
+
+
    +

  1. In risk analysis, the hazard is the risk source where potential consequences relate to harm (Aven et al., n.d.). In the context of dam and levee risk analyses, the source of risk is often defined using a reservoir stage-frequency relationship. The terms hydrologic loading, stage-frequency, and hydrologic hazard are used interchangeably to relate reservoir/river stages to annual exceedance probabilities (AEPs). These relationships are commonly presented as curves and are used as probabilistic loading conditions in dam and levee risk analyses.↩︎

    2. +
+
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/rfaR-book/_book/02-methodology.html b/rfaR-book/_book/02-methodology.html new file mode 100644 index 0000000..e962279 --- /dev/null +++ b/rfaR-book/_book/02-methodology.html @@ -0,0 +1,937 @@ + + + + + + + + + +2  Methodology – rfaR: Reservoir Frequency Analysis in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ + +
+
+

2  Methodology

+
+ + + +
+ + + + +
+ + + +
+ + +
+

2.1 Overview

+

rfaR implements a two-loop, nested Monte Carlo framework to propagate both natural variability and knowledge uncertainty through a deterministic flood routing model.

+
+
+

2.2 Outer Loop: Parameter Uncertainty

+
+
+

2.3 Inner Loop: Realizations

+
+
+

2.4 Flood Routing

+ + + + +
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/rfaR-book/_book/03-installation.html b/rfaR-book/_book/03-installation.html new file mode 100644 index 0000000..a9fc92f --- /dev/null +++ b/rfaR-book/_book/03-installation.html @@ -0,0 +1,956 @@ + + + + + + + + + +3  Installation – rfaR: Reservoir Frequency Analysis in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ + +
+
+

3  Installation

+
+ + + +
+ + + + +
+ + + +
+ + +
+

3.1 Requirements

+
    +
  • R (version ≥ TODO)
    • +
  • Recommended: Positron or RStudio
    • +
+
+
+

3.2 Installing rfaR

+
# From GitHub
+remotes::install_github("USACE-RMC/rfaR")
+

TODO: note CRAN install instructions once published.

+
+
+

3.3 Dependencies

+

TODO: list key dependencies (ggplot2, future/future.apply, lmom, cli, etc.) and any that require special handling on USACE machines.

+
+
+

3.4 Verifying Installation

+
library(rfaR)
+# TODO: smoke-test
+ + + + +
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/rfaR-book/_book/04-workflow.html b/rfaR-book/_book/04-workflow.html new file mode 100644 index 0000000..48d2d26 --- /dev/null +++ b/rfaR-book/_book/04-workflow.html @@ -0,0 +1,953 @@ + + + + + + + + + +4  Workflow – rfaR: Reservoir Frequency Analysis in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ + +
+
+

4  Workflow

+
+ + + +
+ + + + +
+ + + +
+ + +
+

4.1 Overview

+
+
+

4.2 Step 1: Prepare Inputs

+
+
+

4.3 Step 2: Hydrograph Setup

+
+
+

4.4 Step 3: Sampling

+
+
+

4.5 Step 4: Routing

+
+
+

4.6 Step 5: Running the Full Simulation

+
+
+

4.7 Step 6: Interpreting Results

+ + + + +
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/rfaR-book/_book/05-function-reference.html b/rfaR-book/_book/05-function-reference.html new file mode 100644 index 0000000..526668b --- /dev/null +++ b/rfaR-book/_book/05-function-reference.html @@ -0,0 +1,3346 @@ + + + + + + + + + +5  Function Reference – rfaR: Reservoir Frequency Analysis in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ + +
+
+

5  Function Reference

+
+ + + +
+ + + + +
+ + + +
+ + +
+

5.1 Execute Reservoir Frequency Analysis

+
+

5.1.1 rfa_simulate()

+
RFA Stage-Frequency Simulation
+
+Description:
+
+     Performs reservoir stage frequency analysis using Monte Carlo
+     simulation to develop stage-frequency relationships. Combines
+     stratified flow-frequency sampling, flood seasonality, hydrograph
+     scaling, and Modified Puls reservoir routing to estimate annual
+     exceedance probabilities of reservoir stage.
+
+Usage:
+
+     rfa_simulate(
+       sim_type = "expected",
+       bestfit_params,
+       dist = "LP3",
+       stage_ts,
+       seasonality,
+       hydrographs,
+       resmodel,
+       Nbins = 50,
+       events_per_bin = 200,
+       routing_dt = 1,
+       Ncores = NULL,
+       sim_name = NULL,
+       results_dir = NULL
+     )
+     
+Arguments:
+
+sim_type: Character string specifying the simulation mode. One of
+          ‘"median"’, ‘"expected"’, or ‘"full"’. Default is
+          ‘"expected"’.
+
+bestfit_params: Data frame or matrix of distribution parameters from
+          RMC-BestFit MCMC output. Columns 1-3 are distribution
+          parameters:
+
+            * LP3: mean (log), standard deviation (log), skew (log)
+
+            * GEV: location, scale, shape
+
+          Column 4 is log-likelihood (used only in ‘"median"’ mode to
+          identify the posterior mode). For ‘"expected"’ mode, must
+          have at least ‘Nbins * events_per_bin’ rows. For ‘"full"’
+          mode, each row is treated as an independent realization.
+
+    dist: Character string specifying the frequency distribution.
+          Either ‘"LP3"’ (Log-Pearson Type III, default) or ‘"GEV"’
+          (Generalized Extreme Value).
+
+stage_ts: Data frame of historical reservoir stage with columns ‘date’
+          (character in M/D/YYYY format) and ‘stage’ (numeric, feet).
+
+seasonality: Numeric vector of length 12 giving monthly flood
+          occurrence probabilities (relative frequencies). Used to
+          sample the month of each simulated event, which determines
+          the antecedent pool elevation.
+
+hydrographs: List of hydrograph data frames as returned by
+          ‘hydrograph_setup’. Each element has columns ‘datetime’,
+          ‘hour’, ‘inflow’, and ‘hydrograph_num’, with attributes
+          ‘"obs_vol"’ (observed max n-day volume) and ‘"dt"’ (timestep
+          in hours). The list must have a ‘"probs"’ attribute
+          containing normalized sampling probabilities.
+
+resmodel: Data frame with three columns: elevation (ft), storage
+          (acre-ft), and discharge (cfs). Defines the reservoir model
+          for Modified Puls routing.
+
+   Nbins: Integer. Number of stratified sampling bins in EV1 space.
+          Default is 50.
+
+events_per_bin: Integer. Number of events sampled per bin. Default is
+          200. Total simulations per realization = ‘Nbins *
+          events_per_bin’.
+
+routing_dt: Numeric. Routing timestep in hours. Passed to
+          ‘scale_hydrograph’ to resample hydrographs before routing.
+          Supported values are ‘0.25’ (15-min), ‘1’ (1-hour, default),
+          ‘6’ (6-hour), and ‘24’ (24-hour).
+
+  Ncores: Integer or ‘NULL’. Number of parallel workers for ‘"full"’
+          mode. If ‘NULL’ (default), automatically selects based on
+          available cores (capped at 16). Ignored for ‘"median"’ and
+          ‘"expected"’ modes.
+
+sim_name: Optional character string to label the simulation. Used in
+          the output CSV filename. If ‘NULL’ (default), defaults to
+          ‘"sim"’. Spaces are replaced with underscores in the
+          filename.
+
+results_dir: Optional path to the directory where results are saved. If
+          ‘NULL’ (default), creates an ‘rfaR_results’ folder in the
+          current working directory.
+
+Details:
+
+     Three simulation modes are available:
+
+     ‘"median"’ Single realization using the most likely (posterior
+          mode) parameter set from RMC-BestFit. Produces one
+          stage-frequency curve with no uncertainty bounds. Useful for
+          quick screening or debugging.
+
+     ‘"expected"’ Single realization using expected value sampling,
+          where each stratified flow sample is paired 1-to-1 with a
+          different parameter set from the posterior distribution.
+          Produces one stage-frequency curve representing the expected
+          (mean) result integrated across parameter uncertainty.
+
+     ‘"full"’ Nested Monte Carlo with full uncertainty quantification.
+          The outer loop iterates over all parameter sets (one per row
+          of ‘bestfit_params’), and each inner loop independently
+          samples natural variability via stratified sampling,
+          seasonality, starting pool, and hydrograph shape. Each
+          realization produces an independent stage-frequency curve;
+          curves are combined on a common stage grid to yield expected,
+          median, and 5th/95th percentile confidence bounds. The outer
+          loop is parallelized using the ‘future’ framework.
+
+     For all modes, natural variability is captured through stratified
+     sampling in with ‘Nbins’ bins and ‘events_per_bin’ events per bin,
+     providing reliable coverage from common events down to
+     approximately 1e-8 AEP.
+
+     The stratified sampling approach divides the AEP range (default
+     0.99 to 1e-8) into equal-width bins in EV1 (Gumbel reduced
+     variate) space and samples events uniformly within each bin. This
+     ensures adequate representation of rare flood events that would
+     require orders of magnitude more samples under crude Monte Carlo
+     sampling.
+
+     Post-processing uses the law of total probability via
+     ‘stage_frequency_curve’: for a grid of stage thresholds, the
+     weighted exceedance fraction is computed within each bin and
+     summed across bins.
+
+     In ‘"full"’ mode, each parallel worker independently samples all
+     random inputs (seasonality, starting pool, hydrograph shape, and
+     stratified z-variates) in addition to using its own parameter set.
+     This ensures full Monte Carlo independence across realizations.
+     Each worker post-processes its results into a stage-frequency
+     curve immediately, returning only the curve rather than the full
+     peak stage matrix, keeping memory usage manageable for large
+     numbers of realizations.
+
+     Curves from individual realizations are combined by interpolating
+     each onto a common stage grid (spanning the global min/max across
+     all realizations) in log10(AEP) space, then computing summary
+     statistics across realizations at each stage threshold.
+
+     Results are automatically exported as a CSV file named
+     ‘{sim_name}_{sim_type}_{MM_DD_YY_HHMM}.csv’ in the ‘results_dir’
+     directory. For example, a median simulation named "John McGraw
+     Dam" run on June 4, 2025 at 2:30 PM would produce
+     ‘John_McGraw_Dam_median_06_04_25_1430.csv’.
+
+Value:
+
+     A list whose contents depend on ‘sim_type’:
+
+     For ‘"median"’ and ‘"expected"’:
+
+     stage_frequency Data frame with columns ‘stage’ and ‘AEP’ from
+          ‘stage_frequency_curve’.
+
+     peakStage Matrix of simulated peak stages (‘events_per_bin’ rows
+          by ‘Nbins’ columns).
+
+     peakFlow Matrix of simulated peak discharges (same dimensions).
+
+     weights Stratified sampling bin weights.
+
+     For ‘"full"’:
+
+     stage_frequency Data frame with columns ‘stage’, ‘expected’,
+          ‘median’, ‘lower_05’, and ‘upper_95’ on a common stage grid.
+
+     all_curves List of per-realization stage-frequency data frames
+          (each with ‘stage’ and ‘AEP’ columns).
+
+     aep_matrix Matrix of interpolated AEP values (500 rows by
+          ‘Nrealizations’ columns) on the common stage grid.
+
+     common_stage Numeric vector of the common stage grid (length 500).
+
+     Nrealizations Number of parameter set realizations processed.
+
+     Nsims_per_realiz Number of simulations per realization.
+
+See Also:
+
+     ‘flow_frequency_sampler’, ‘flow_frequency_sampler_expected’,
+     ‘stratified_sampler’, ‘scale_hydrograph’, ‘mod_puls_routing’,
+     ‘stage_frequency_curve’, ‘hydrograph_setup’
+
+Examples:
+
+     ## Not run:
+     
+     # Not run: Expected and Median modes take ~15s; Full uncert
+     # runs ~10,000 parameter realizations and takes 3-4 hours on a
+     # typical machine.
+     
+     # --- Setup ---
+     hydros <- hydrograph_setup(jmd_hydro_apr1999, jmd_hydro_jun1965,
+                                jmd_hydro_may1955, jmd_hydro_pmf,
+                                critical_duration = 2, routing_days = 10)
+     
+     # --- Expected only (default) ---
+     results_exp <- rfa_simulate(
+       sim_type       = "expected",
+       bestfit_params = jmd_bf_parameter_sets,
+       stage_ts       = jmd_wy1980_stage,
+       seasonality    = jmd_seasonality$relative_frequency,
+       hydrographs    = hydros,
+       resmodel       = jmd_resmodel,
+       sim_name       = "jmd"
+     )
+     
+     # --- Median only ---
+     results_med <- rfa_simulate(
+       sim_type       = "median",
+       bestfit_params = jmd_bf_parameter_sets,
+       stage_ts       = jmd_wy1980_stage,
+       seasonality    = jmd_seasonality$relative_frequency,
+       hydrographs    = hydros,
+       resmodel       = jmd_resmodel,
+       sim_name       = "jmd"
+     )
+     
+     # --- Full uncertainty (parallelized) ---
+     results_full <- rfa_simulate(
+       sim_type       = "full",
+       bestfit_params = jmd_bf_parameter_sets,
+       stage_ts       = jmd_wy1980_stage,
+       seasonality    = jmd_seasonality$relative_frequency,
+       hydrographs    = hydros,
+       resmodel       = jmd_resmodel,
+       Ncores         = 4,
+       sim_name       = "jmd"
+     )
+     ## End(Not run)
+
+
+
+
+

5.2 Primary RFA Modules

+
+

5.2.1 stratified_sampler()

+
Stratified Sampler
+
+Description:
+
+     Creates stratified bins in standard normal space for use in
+     stratified sampling. Stratification improves sampling efficiency
+     by ensuring adequate coverage of rare events.
+
+Usage:
+
+     stratified_sampler(
+       minAEP = 1e-08,
+       maxAEP = 0.99,
+       dist = "EV1",
+       Nbins = NULL,
+       Mevents = NULL,
+       verbose = FALSE
+     )
+     
+Arguments:
+
+  minAEP: Minimum annual exceedance probability. Default is ‘1E-8’.
+
+  maxAEP: Maximum annual exceedance probability. Default is ‘0.99’.
+
+    dist: Character. Probability space for stratification.
+          Case-insensitive. Default is ‘"EV1"’. Invalid values trigger
+          a warning and default to ‘"EV1"’.
+
+          ‘"EV1"’ Extreme Value Type I (Gumbel) space. Recommended for
+              flood frequency analysis. Allocates more bins to rare
+              events through the transformation ‘-log(-log(1-AEP))’,
+              improving tail estimation efficiency.
+
+          ‘"Normal"’ Standard normal (z-score) space. Uniform bins in
+              z-space. Use when the underlying phenomenon is normally
+              distributed.
+
+          ‘"Uniform"’ Uniform probability space. Equal probability
+              width per bin. Generally inefficient for rare event
+              estimation.
+
+   Nbins: Number of stratified bins. Default is ‘20’.
+
+ Mevents: Number of events per bin. Default is ‘500’.
+
+ verbose: Logical. If ‘TRUE’, prints a completion message summarizing
+          the stratification. Default is ‘FALSE’.
+
+Details:
+
+     The function divides the probability space into bins using the
+     specified transformation. EV1 transformation is recommended for
+     heavy-tailed distributions common in flood frequency analysis, as
+     it naturally allocates more sampling effort to rare events
+     critical for dam safety assessments.
+
+Value:
+
+     A list containing:
+
+     normOrd Vector of standard normal ordinates spanning all bins
+
+     Weights Vector of probability weights for each bin
+
+     Zlower Vector of lower bounds for each bin (standard normal)
+
+     Zupper Vector of upper bounds for each bin (standard normal)
+
+     Nbins Number of bins
+
+     Mevents Number of events per bin
+
+Examples:
+
+     # Default stratification
+     strat <- stratified_sampler()
+     
+     # Custom bins and events
+     strat <- stratified_sampler(minAEP = 1E-6,
+                                 maxAEP = 0.5,
+                                 dist = "EV1",
+                                 Nbins = 10,
+                                 Mevents = 100)
+     
+     # With verbose message
+     strat <- stratified_sampler(verbose = TRUE)
+
+
+
+

5.2.2 flow_frequency_sampler()

+
Flow Frequency Sampler
+
+Description:
+
+     Generates a stratified matrix of flow values from a single set of
+     frequency distribution parameters using stratified Monte Carlo
+     sampling. Used internally by ‘rfa_simulate()’ for median-only and
+     full uncertainty modes.
+
+Usage:
+
+     flow_frequency_sampler(
+       bestfit_params,
+       freq_dist = "LP3",
+       strat_dist = "ev1",
+       Nbin = NULL,
+       Mevent = NULL
+     )
+     
+Arguments:
+
+bestfit_params: Numeric vector of length 3 containing distribution
+          parameters. For LP3: ‘c(mean_log, sd_log, skew_log)’. For
+          GEV: ‘c(location, scale, shape)’.
+
+freq_dist: Character. Distribution type. Either ‘"LP3"’ (default) or
+          ‘"GEV"’.
+
+strat_dist: Character. Probability space for stratification bins.
+          Passed to ‘stratified_sampler()’. One of ‘"ev1"’ (default),
+          ‘"normal"’, or ‘"uniform"’. See ‘stratified_sampler()’ for
+          details.
+
+    Nbin: Integer. Number of stratified bins. Default is ‘50’.
+
+  Mevent: Integer. Number of events per bin. Default is ‘200’.
+
+Value:
+
+     A list containing:
+
+     flow Matrix of sampled flow values [Mevent x Nbin]
+
+     nbins Number of stratified bins
+
+     mevents Number of events per bin
+
+     weights Probability weights for each bin from
+          ‘stratified_sampler()’
+
+See Also:
+
+     ‘stratified_sampler()’, ‘qp3()’, ‘rfa_simulate()’
+
+Examples:
+
+     # Single parameter set (posterior mode)
+     params <- c(4.85, 0.39, -0.15)
+     result <- flow_frequency_sampler(params, freq_dist = "LP3",
+                                      Nbin = 20, Mevent = 500)
+     # Dimensions of result
+     dim(result$flow)  # 500 x 20
+     
+     # Distribution of Sampled Flows
+     hist(result$flow)
+
+
+
+

5.2.3 flow_frequency_sampler_expected()

+
Flow Frequency Sampler (Expected Only)
+
+Description:
+
+     Generates a stratified matrix of flow values by pairing each
+     z-ordinate with a different parameter set from the posterior
+     distribution. This collapses the nested Monte Carlo structure into
+     a single pass, simultaneously sampling natural variability (via
+     stratified z-ordinates) and knowledge uncertainty (via varying
+     parameters). Used internally by ‘rfa_simulate()’ for expected-only
+     mode.
+
+Usage:
+
+     flow_frequency_sampler_expected(
+       bestfit_params,
+       freq_dist = "LP3",
+       strat_dist = "ev1",
+       Nbin = NULL,
+       Mevent = NULL
+     )
+     
+Arguments:
+
+bestfit_params: Data frame of distribution parameters from RMC-BestFit.
+          Must have ‘Nbin * Mevent’ rows (one parameter set per
+          z-ordinate). For LP3: columns are mean (log), sd (log), skew
+          (log). For GEV: columns are location, scale, shape.
+
+freq_dist: Character. Distribution type. Either ‘"LP3"’ (default) or
+          ‘"GEV"’.
+
+strat_dist: Character. Probability space for stratification bins.
+          Passed to ‘stratified_sampler()’. One of ‘"ev1"’ (default),
+          ‘"normal"’, or ‘"uniform"’. See ‘stratified_sampler()’ for
+          details.
+
+    Nbin: Integer. Number of stratified bins. Default is ‘50’.
+
+  Mevent: Integer. Number of events per bin. Default is ‘200’.
+
+Value:
+
+     A list containing:
+
+     flow Matrix of sampled flow values [Mevent x Nbin]
+
+     nbins Number of stratified bins
+
+     mevents Number of events per bin
+
+     weights Probability weights for each bin from
+          ‘stratified_sampler()’
+
+See Also:
+
+     ‘stratified_sampler()’, ‘qp3()’, ‘flow_frequency_sampler()’,
+     ‘rfa_simulate()’
+
+Examples:
+
+     # Using a pre-loaded parameter set (all 10,000 parameter sets)
+     result <- flow_frequency_sampler_expected(jmd_bf_parameter_sets,
+                                               Nbin = 20, Mevent = 500)
+     dim(result$flow)  # 500 x 20
+     
+     # Using bootstrapped parameter samples
+     jmd_samples <- bootstrap_vfc(
+       c(jmd_vfc_parameters$mean_log,
+         jmd_vfc_parameters$sd_log,
+         jmd_vfc_parameters$skew_log),
+       dist = "LP3",
+       ERL  = jmd_vfc_parameters$erl)
+     
+     jmd_result <- flow_frequency_sampler_expected(
+       jmd_samples$params,
+       freq_dist = "LP3",
+       Nbin      = 20,
+       Mevent    = 500)
+
+
+
+

5.2.4 hydrograph_setup()

+
Hydrograph Setup for RFA Simulation
+
+Description:
+
+     Prepares hydrograph data frames copied from RMC-RFA for use in RFA
+     simulation. Converts date/time columns and adds sequential hour
+     and hydrograph ID columns.
+
+Usage:
+
+     hydrograph_setup(
+       ...,
+       critical_duration = NULL,
+       routing_days = NULL,
+       weights = NULL
+     )
+     
+Arguments:
+
+     ...: Data frame representing an input hydrograph with columns:
+          Ord, Date, Time, Flow (cfs). Copied directly from RMC-RFA.
+          Date & Time should be ‘class() = "character"’
+
+critical_duration: Critical duration in days.
+
+routing_days: Desired length of routing simulation in days.
+
+ weights: Optional numeric vector of sampling weights for each
+          hydrograph. Must be the same length as the number of input
+          hydrographs. Weights are normalized to probabilities
+          internally. If NULL (default), all hydrographs are weighted
+          equally.
+
+Value:
+
+     A list of formatted hydrograph data frames, each containing:
+
+     datetime Date-time (POSIXct)
+
+     hour Hours from start of event
+
+     inflow Inflow (cfs)
+
+     hydrograph_num Hydrograph ID number for sampling
+
+     obs_vol Max n-day inflow volume (stored as an attribute of each
+          dataframe)
+
+     dt Hydrograph timestep (delta time, dt) in hours (stored as an
+          attribute of each dataframe)
+
+     The returned list also has a ‘probs’ attribute containing the
+     normalized sampling probabilities derived from ‘weights’.
+
+Examples:
+
+     # Setup with equal weights (default)
+     hydros <- hydrograph_setup(jmd_hydro_apr1999,
+                                jmd_hydro_jun1965,
+                                jmd_hydro_pmf,
+                                critical_duration = 2,
+                                routing_days = 10)
+     
+     # Setup with custom weights (PMF 3x more likely to be sampled)
+     hydros <- hydrograph_setup(jmd_hydro_apr1999,
+                                jmd_hydro_jun1965,
+                                jmd_hydro_pmf,
+                                critical_duration = 2,
+                                routing_days = 10,
+                                weights = c(1, 1, 3))
+     
+     # view normalized probabilities
+     attr(hydros, "probs")
+
+
+
+

5.2.5 scale_hydrograph()

+
Scale Hydrograph
+
+Description:
+
+     Scales inflow hydrograph given a sampled inflow volume from the
+     volume-frequency curve. Scale factor is defined by the sampled
+     inflow volume and the maximum volume of the corresponding
+     duration. If the native hydrograph timestep differs from the
+     target routing timestep, resampling is applied prior to scaling:
+     finer-to-coarser uses block averaging; coarser-to-finer uses
+     linear interpolation.
+
+Usage:
+
+     scale_hydrograph(
+       hydrograph_shape,
+       observed_volume,
+       sampled_volume,
+       routing_dt = 1
+     )
+     
+Arguments:
+
+hydrograph_shape: Data frame with two columns: time (hours) and inflow
+          (cfs). Must be in that order.
+
+observed_volume: Maximum n-day inflow volume from input hydrograph
+          shape.
+
+sampled_volume: Sampled inflow volume from volume-frequency curve.
+
+routing_dt: Target routing timestep in hours. Supported values are
+          ‘0.25’ (15-min), ‘1’ (1-hour, default), ‘6’ (6-hour), and
+          ‘24’ (24-hour).
+
+Value:
+
+     A data frame with two columns: ‘time_hrs’ and ‘inflow_cfs’, at the
+     target routing timestep, with time starting at hour 0.
+
+Examples:
+
+     # Example hydrograph. Requires pre-processing
+     hydro_example <- hydrograph_setup(jmd_hydro_jun1965_15min, critical_duration = 2, routing_days = 10)
+     hydrograph_shape <- hydro_example[[1]][, 2:3]
+     
+     # Default 1-hour routing timestep
+     scaled <- scale_hydrograph(hydrograph_shape,
+                                observed_volume = 50000,
+                                sampled_volume = 55000)
+     
+     # 15-min input hydrograph, 1-hour routing timestep
+     scaled <- scale_hydrograph(hydrograph_shape,
+                                observed_volume = 50000,
+                                sampled_volume = 55000,
+                                routing_dt = 1)
+
+
+
+

5.2.6 mod_puls_routing()

+
Modified Puls Reservoir Routing
+
+Description:
+
+     Performs Modified Puls (level pool) routing of inflow hydrograph
+     given a defined reservoir geometry (Stage (ft), Storage (ac-ft),
+     Discharge (cfs)).
+
+Usage:
+
+     mod_puls_routing(resmodel_df, inflow_df, initial_elev, full_results = FALSE)
+     
+Arguments:
+
+resmodel_df: Data frame with three columns: elevation/stage (ft),
+          storage (acre-feet), and discharge (cfs). Must be in that
+          order.
+
+inflow_df: Data frame with two columns: time (hours) and inflow (cfs).
+          Must be in that order.
+
+initial_elev: Starting water surface elevation in feet.
+
+full_results: Logical. If ‘FALSE’ (default), returns only peak stage
+          and discharge. If ‘TRUE’, returns the complete routing
+          result.
+
+Value:
+
+     If ‘full_results = FALSE’, a named numeric vector with
+     ‘peak_stage_ft’ and ‘peak_discharge_cfs’. If ‘full_results =
+     TRUE’, a data frame with columns: ‘time_hr’, ‘inflow_cfs’,
+     ‘elevation_ft’, ‘storage_acft’, and ‘outflow_cfs’.
+
+References:
+
+     Chow, V.T. (1959). Open-Channel Hydraulics. McGraw-Hill.
+
+Examples:
+
+     # Example hydrograph. Requires pre-processing
+     hydro_example <- hydrograph_setup(jmd_hydro_jun1965_15min,
+                                        critical_duration = 2,
+                                        routing_days = 10)
+     hydrograph_shape <- hydro_example[[1]][, 2:3]
+     scaled_hydrograph <- scale_hydrograph(hydrograph_shape,
+                                           observed_volume = 50000,
+                                           sampled_volume = 55000)
+     
+     # Peak values only
+     mod_puls_routing(jmd_resmodel, scaled_hydrograph, initial_elev = 3830)
+     
+     # Full routing table
+     jmd_full_routing <- mod_puls_routing(jmd_resmodel,
+                                          scaled_hydrograph,
+                                          initial_elev = 3830,
+                                          full_results = TRUE)
+     head(jmd_full_routing)
+
+
+
+

5.2.7 stage_frequency_curve()

+
Compute Stage-Frequency Curve
+
+Description:
+
+     Converts a matrix of routed peak stages into a stage-frequency
+     curve using the law of total probability across stratified bins.
+     For each candidate stage, the conditional exceedance probability
+     within each bin is weighted by the bin probability and summed to
+     produce the unconditional annual exceedance probability (AEP).
+
+Usage:
+
+     stage_frequency_curve(peakStage, weights, stage_bins = 1000)
+     
+Arguments:
+
+peakStage: Matrix of peak stages [Mevents x Nbins] from routing.
+
+ weights: Numeric vector of bin weights from ‘stratified_sampler()’.
+          Must have length equal to ‘ncol(peakStage)’.
+
+stage_bins: Numeric value of exceedance stages used to compute
+          stage-frequency in each bin. Default is 1000.
+
+Value:
+
+     A data frame with columns:
+
+     stage Evaluated stage values
+
+     AEP Annual exceedance probability at each stage
+
+See Also:
+
+     ‘stratified_sampler()’, ‘flow_frequency_sampler()’,
+     ‘rfa_simulate()’
+
+
+
+
+

5.3 Rejection Sampling

+
+

5.3.1 pmf_stage_lognormal()

+
Parameterize a Three-Parameter Lognormal PMF for Stage
+
+Description:
+
+     Computes the parameters of a three-parameter lognormal
+     distribution for use as a probabilistic maximum stage (PMF) in
+     rejection sampling. The shift parameter defines the lower or upper
+     bound of the distribution (typically, the lower value). The
+     function supports two modes: (1) supplying a best estimate and
+     assuming sigma, or (2) supplying a best estimate, low, and high to
+     solve for sigma numerically.
+
+Usage:
+
+     pmf_stage_lognormal(
+       pmf_shift,
+       pmf_mean,
+       pmf_sigma = NULL,
+       pmf_low = NULL,
+       pmf_high = NULL
+     )
+     
+Arguments:
+
+pmf_shift: Numeric. Assumed lower-bound of the lognormal distribution.
+          This will define the shift value. Must be less than all of
+          ‘pmf_mean’, ‘pmf_low’, and ‘pmf_high’.
+
+pmf_mean: Numeric. Assumed mean of the shifted PMF stage distribution.
+
+pmf_sigma: Numeric. Assumed standard deviation on the log scale.
+          Required if ‘pmf_low’ and ‘pmf_high’ are not supplied.
+          Defaults to ‘NULL’. Suggested value = 0.5.
+
+ pmf_low: Numeric. Low estimate of PMF stage (assumed 5th percentile).
+          Optional. If supplied, ‘pmf_high’ must also be supplied.
+
+pmf_high: Numeric. High estimate of PMF stage (assumed 95th
+          percentile). Optional. If supplied, ‘pmf_low’ must also be
+          supplied.
+
+Value:
+
+     A named list with the following elements:
+
+     pmf_shift Hard lower bound of the distribution.
+
+     pmf_mean Assumed mean of shifted PMF stage distribution.
+
+     pmf_sigma Standard deviation on the log scale.
+
+     pmf_mu Derived location parameter on the log scale.
+
+     pmf_p05 5th percentile of the PMF stage distribution.
+
+     pmf_p95 95th percentile of the PMF stage distribution.
+
+Examples:
+
+     # Mode 1: assume sigma
+     pmf <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, pmf_sigma = 0.5)
+     
+     # Mode 2: solve for sigma from low/high estimates
+     pmf <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9,
+                                 pmf_low = 239, pmf_high = 245)
+
+
+
+

5.3.2 rejection_sampling_stage()

+
Rejection Sampling of Stage Bounded by a Probabilistic Maximum Stage
+
+Description:
+
+     Draws ‘n_samples’ stage values from a stage-frequency curve,
+     rejecting any draws that exceed a probabilistic maximum stage
+     (PMF) sampled from a three-parameter lognormal distribution.
+     Accepted samples are ranked and assigned Weibull plotting
+     positions for use in frequency analysis.
+
+Usage:
+
+     rejection_sampling_stage(
+       pmf_stage_LN,
+       stage_freq_df = NULL,
+       aep = NULL,
+       stage = NULL,
+       n_samples = 1e+07
+     )
+     
+Arguments:
+
+pmf_stage_LN: A named list returned by ‘pmf_stage_lognormal’ containing
+          the lognormal PMF parameters.
+
+stage_freq_df: A data frame with AEP in column 1 and stage in column 2.
+          Mutually exclusive with ‘aep’ and ‘stage’.
+
+     aep: Numeric vector of annual exceedance probabilities. Must be
+          supplied with ‘stage’. Mutually exclusive with
+          ‘stage_freq_df’.
+
+   stage: Numeric vector of stages corresponding to ‘aep’. Must be
+          supplied with ‘aep’. Mutually exclusive with ‘stage_freq_df’.
+
+n_samples: Integer. Number of samples to draw. Defaults to ‘1e7’.
+
+Value:
+
+     A data frame of thinned accepted samples with columns for z-score
+     and stage, suitable for plotting a probabilistically bounded
+     stage-frequency curve. Output is produced by ‘thin_samples()’.
+
+See Also:
+
+     ‘pmf_stage_lognormal’
+
+Examples:
+
+     ## Not run:
+     
+     pmf_ln <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, pmf_sigma = 0.5)
+     
+     result <- rejection_sampling_stage(
+       pmf_stage_LN  = pmf_ln,
+       stage_freq_df = my_stage_freq_df,
+       n_samples     = 1e7
+     )
+     ## End(Not run)
+
+
+
+
+

5.4 Additional Utilities

+
+

5.4.1 bootstrap_vfc()

+
Bootstrap option for VFC (RFA-style w/ ERL)
+
+Description:
+
+     Generates matrix of parameters using posterior mode/mean and ERL
+
+Usage:
+
+     bootstrap_vfc(bestfit_postmode, dist = "LP3", ERL = 150, Nboots = 10000)
+     
+Arguments:
+
+bestfit_postmode: Vector of distribution parameters from RMC-BestFit.
+          For LP3: columns are mean (log), sd (log), skew (log). For
+          GEV: columns are location, scale, shape.
+
+    dist: Distribution type. Either ‘"LP3"’ (default) or ‘"GEV"’.
+
+     ERL: psuedo effective record length for bootstrapping
+
+  Nboots: number of bootstraps
+
+Value:
+
+     A list containing:
+
+     params Matrix of bootstrapped parameters
+
+     dist Distribution selected (LP3 or GEV)
+
+     postmode Posterior mode parameters provided to bootstrap
+
+     ERL pseudo effective record length provided to bootstrap
+
+See Also:
+
+     ‘flow_frequency_sampler()’
+
+Examples:
+
+     # Sample using JMD VFC parameters
+     jmd_samples <- bootstrap_vfc(c(jmd_vfc_parameters$mean_log,
+                                     jmd_vfc_parameters$sd_log,
+                                     jmd_vfc_parameters$skew_log),
+                                     dist = "LP3",
+                                     ERL = jmd_vfc_parameters$erl)
+     
+     # GEV Example with parent distribution parameters
+     gev_example <- c(3.0, 1.0, -0.1)
+     gev_samples <- bootstrap_vfc(gev_example,
+                                   dist = "GEV",
+                                   ERL = 200,
+                                   Nboots = 5000)
+     hist(gev_samples$params[,1])
+     
+     # LP3 Example with parent distribution parameters
+     lp3_example <- c(3.5, 0.22, 0.1)
+     lp3_samples <- bootstrap_vfc(lp3_example,
+                                   dist = "LP3",
+                                   ERL = 300,
+                                   Nboots = 1000)
+     hist(lp3_samples$params[,3])
+
+
+
+

5.4.2 qp3()

+
Pearson Type III Inverse CDF (Quantile Function)
+
+Description:
+
+     Computes quantiles from a Pearson Type III distribution given
+     probabilities and distribution parameters (mean, standard
+     deviation, skewness).
+
+Usage:
+
+     qp3(p, mu, sigma, gamma)
+     
+Arguments:
+
+       p: Vector of probabilities (between 0 and 1).
+
+      mu: Mean of the distribution.
+
+   sigma: Standard deviation of the distribution.
+
+   gamma: Skewness coefficient of the distribution.
+
+Details:
+
+     NOTE: This is the non-vectorized version of the function (it will
+     be used in a loop) Vectorized version is part of future
+     development.
+
+     The Pearson Type III distribution is parameterized by mean (‘mu’),
+     standard deviation (‘sigma’), and skewness (‘gamma’). These are
+     converted internally to location, scale, and shape parameters.
+
+     When skewness is near zero (‘abs(gamma) < 1E-3’), the normal
+     distribution is used as an approximation.
+
+Value:
+
+     Vector of quantiles corresponding to the input probabilities.
+
+Examples:
+
+     # Single quantile
+     qp3(0.99, mu = 10, sigma = 2, gamma = 0.5)
+     
+     # Multiple quantiles
+     qp3(c(0.5, 0.9, 0.99), mu = 10, sigma = 2, gamma = 0.5)
+     
+     # Use with JMD volume-frequency parameters
+     qp3(0.99,
+         mu = jmd_vfc_parameters$mean_log,
+         sigma = jmd_vfc_parameters$sd_log,
+         gamma = jmd_vfc_parameters$skew_log)
+
+
+
+

5.4.3 aep2stage()

+
Interpolate Stage from AEP
+
+Description:
+
+     Given a stage-frequency curve, interpolates stage values at target
+     AEPs. Interpolation is performed in standard normal (z-variate)
+     space for better behavior across orders of magnitude in AEP.
+
+Usage:
+
+     aep2stage(aep, stage, interp_aep)
+     
+Arguments:
+
+     aep: Numeric vector of annual exceedance probabilities from the
+          known curve.
+
+   stage: Numeric vector of stage values corresponding to ‘aep’.
+
+interp_aep: Numeric vector of target AEPs to interpolate at.
+
+Value:
+
+     Numeric vector of interpolated stage values (same length as
+     ‘interp_aep’).
+
+See Also:
+
+     ‘stage2aep’ for the inverse operation.
+
+Examples:
+
+     # Get stage at the 1.12% and 0.175% AEP
+     aep2stage(jmd_rfa_expected$AEP, jmd_rfa_expected$Expected, c(0.0112,1.75E-3))
+
+
+
+

5.4.4 stage2aep()

+
Interpolate AEP from Stage
+
+Description:
+
+     Given a stage-frequency curve, interpolates AEP values at target
+     stages. Interpolation is performed in standard normal (z-variate)
+     space for better behavior across orders of magnitude in AEP.
+
+Usage:
+
+     stage2aep(aep, stage, interp_stage)
+     
+Arguments:
+
+     aep: Numeric vector of annual exceedance probabilities from the
+          known curve.
+
+   stage: Numeric vector of stage values corresponding to ‘aep’.
+
+interp_stage: Numeric vector of target stage values to interpolate at.
+
+Details:
+
+     Stage-frequency curves typically flatten at very rare AEPs once
+     the reservoir reaches the reservoir model discharge capacity. This
+     produces tied stage values. ‘approx()’ handles these by averaging
+     the corresponding AEPs, which may emit a "collapsing to unique 'x'
+     values" warning. This is expected behavior.
+
+Value:
+
+     Numeric vector of interpolated AEP values (same length as
+     ‘interp_stage’).
+
+See Also:
+
+     ‘aep2stage’ for the inverse operation.
+
+Examples:
+
+     # Get AEP at stage 3862.0-ft and 3872-ft
+     stage2aep(jmd_rfa_expected$AEP, jmd_rfa_expected$Expected, c(3862.0, 3872))
+
+
+
+

5.4.5 interpolate_aep_matrix()

+
Interpolate AEPs at Target Stages Across Realizations
+
+Description:
+
+     For each realization's stage-frequency curve, interpolates the AEP
+     at every target stage value. Interpolation is performed in
+     standard normal (z-variate) space.
+
+Usage:
+
+     interpolate_aep_matrix(results_list, target_stages)
+     
+Arguments:
+
+results_list: List of realization data frames, each with columns
+          ‘stage’ and ‘AEP’.
+
+target_stages: Numeric vector of stage values to interpolate at.
+
+Value:
+
+     Matrix of AEP values with rows = target stages and columns =
+     realizations.
+
+
+
+

5.4.6 interpolate_stage_matrix()

+
Interpolate Stages at Target AEPs Across Realizations
+
+Description:
+
+     For each realization's stage-frequency curve, interpolates the
+     stage at every target AEP value. Interpolation is performed in
+     standard normal (z-variate) space.
+
+Usage:
+
+     interpolate_stage_matrix(results_list, target_aeps)
+     
+Arguments:
+
+results_list: List of realization data frames, each with columns
+          ‘stage’ and ‘AEP’.
+
+target_aeps: Numeric vector of AEP values to interpolate at.
+
+Value:
+
+     Matrix of stage values with rows = target AEPs and columns =
+     realizations.
+
+
+
+

5.4.7 power_function()

+
Power Function
+
+Description:
+
+     Raises 10 to the 1st through the nth power
+
+Usage:
+
+     power_function(x)
+     
+Arguments:
+
+       x: Highest power of 10 to use
+
+Value:
+
+     Vector of 10 raised sequentially from the 1st to the nth power.
+
+
+
+

5.4.8 theme_rfar_conceptual()

+
rfaR ggplot2 Theme for Conceptual Plots
+
+Description:
+
+     A custom ‘ggplot2’ theme used in the rfaR conceptual realization
+     vignette. Based on ‘theme_bw’ with tightened text sizes, italic
+     titles, and the legend suppressed. Intended for figures that
+     emphasize curves over annotation density.
+
+Usage:
+
+     theme_rfar_conceptual()
+     
+Value:
+
+     A ‘ggplot2’ theme object that can be added to a ‘ggplot’ via ‘+’.
+
+See Also:
+
+     ‘theme_bw’
+
+Examples:
+
+     library(ggplot2)
+     ggplot(jmd_rfa_expected, aes(x = AEP, y = Expected)) +
+       geom_line() +
+       scale_x_continuous(transform = c("log10", "reverse")) +
+       labs(title = "JMD Expected Stage-Frequency",
+            subtitle = "Demonstrating theme_rfar_conceptual()",
+            x = "AEP", y = "Stage (ft)") +
+       theme_rfar_conceptual()
+
+ + + + +
+
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/rfaR-book/_book/06-simulation-modes.html b/rfaR-book/_book/06-simulation-modes.html new file mode 100644 index 0000000..d09cf53 --- /dev/null +++ b/rfaR-book/_book/06-simulation-modes.html @@ -0,0 +1,939 @@ + + + + + + + + + +6  Simulation Modes – rfaR: Reservoir Frequency Analysis in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ + +
+
+

6  Simulation Modes

+
+ + + +
+ + + + +
+ + + +
+ + +

rfaR supports three simulation modes, corresponding to different treatments of parameter uncertainty.

+
+

6.1 Median Mode

+
+
+

6.2 Expected Mode

+
+
+

6.3 Full Uncertainty Mode

+

Runs the full nested Monte Carlo framework, propagating both parameter (epistemic) and event (aleatory) uncertainty.

+
+
+

6.4 Selecting a Mode

+ + + + +
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/rfaR-book/_book/07-validation.html b/rfaR-book/_book/07-validation.html new file mode 100644 index 0000000..2198421 --- /dev/null +++ b/rfaR-book/_book/07-validation.html @@ -0,0 +1,931 @@ + + + + + + + + + +7  Validation – rfaR: Reservoir Frequency Analysis in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ +
+ + +
+ + + +
+ + +
+
+

7  Validation

+
+ + + +
+ + + + +
+ + + +
+ + +
+

7.1 Purpose

+
+
+

7.2 Modified Puls Routing

+
+
+

7.3 Stratified Sampling

+
+
+

7.4 See Also

+

Detailed validation vignettes are also available on the pkgdown site.

+ + + + +
+ +
+ + +
+ + + + + + \ No newline at end of file diff --git a/rfaR-book/_book/cover.jpg b/rfaR-book/_book/cover.jpg new file mode 100644 index 0000000000000000000000000000000000000000..f42d2d65d307116dbd70dcbad2cabed00e3db4fb GIT binary patch literal 120849 zcmb3fI$HYM zJnest5d2q@h?xAgBJCZDJGZ6(Px#jZ0Fo0P6Z|A1-~kW<35b9M|M~zN004lD;1+Jv z{{=!KViGcP3Q_<8a-i^w z1~N*b%Np^&nYcmfz2Z{Fj;r_hRH26cF-H%iP3?U%GPBCxG0VszOq>FuVo{|{AE#L~ zoZ~PJpJw>ATmt*n4c_md1?R}%7QRvdfkJF50y(=JfWBehRKa-T>sx%_z z=pG-`DWLq*zXbpl(QSu7A|T)qpt0!PLnHq?>E`H9t{-7vDeh;rnI&H@PtD0kH_Xtp zzKOWuy*}Sx|JiB}j7M`8WX)7w?>)NG|5JPp`+M5%B-7EFZ1ai;Iv8fFKR#hJSBA{7S{dOJS1Y< zy_O#<4N!vuD6=~qFP31_zWIDqOU|?-7&n_52{V@G!B&Avw6K~KIt+~*(Q*C*hgmzGs2*7wVND45d^kUDzu6#3dbG(h%uDetGZDY!O& zj2bm-?A`g%1Z@dX{0mQgp$pzqTnA#&=HH%N5;3$4vhKWZ)){wPm%O^I`Z zeEM44nqH?p#?Kk1JEc(bjLdOUL$jA=tBOb;2g5mOM?xK0d3??@9s=Xo{fTCUJ9~~L~Z0-G)O=889;pG*M0YZ__m?h zTe|kh)#TrZ<>pijg+5wyQ@K*RxOS(M{G?{I)3o;wfW_q(#7G_IWZU;ZWB<0{24FL*2t2luy$~l+dsfnQ+j79dl^zfDvioBv&;3i 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zo(7Ob`%kyF%FWs+xg5Y@kzoA%v{%b?<*Q?Y^ z5#xezU;B@-{{YR%{u=xG7lVJ?jg$UvNATCH)q=5o{SCqY0Paf5{{T0m_-o+c+#mkr z?7#DRUxvM2p;&|%I|y(e)rBnFKlB8@4Sf9*!k_M1&;I}*OYqmL)re5PL~xh;m9u}y z68tsq(HtfIy;z9@^j8aixr;yifiJ^C3@L?pe`aJT!Iw13DF{59{LrWAWU=x80P0J2-}})&{#0^yD<1_uUKU$_4nO7Y zq5lBNy + + + + + + + + + + + + +rfaR: Reservoir Frequency Analysis in R + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    rfaR: Reservoir Frequency Analysis in R

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    User and Technical Manual

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    Authors
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    Dan McGraw, P.E.

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    Andrew Verdin, PhD, P.H.

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    C. Haden Smith, P.E.

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    Published
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    August 14, 2026

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    Preface

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    rfaR implements the RMC Reservoir Frequency Analysis (RMC-RFA) methodology in R, producing reservoir stage-frequency curves with uncertainty bounds by combining deterministic Modified Puls flood routing with a nested Monte Carlo framework.

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    This manual complements the function reference site generated by pkgdown. Where the pkgdown site documents individual functions, this manual explains the methodology, workflow, and how the pieces fit together.

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    How to cite

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    Cover image: “Grandma’s Boy” (1922) movie poster, public domain, via Wikimedia Commons.

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Each realization represents 10,000 indiviual flood events with randomly sampled starting stage, hydrograph shape, and inflow volume (from an associated frequency distribution).\nKnowledge uncertainty (epistemic uncertainty) — simulated in the outer loop, by sampling different parameter sets (from LP3 or GEV flood frequency distributions, informed by MCMC output from RMC-BestFit).", + "crumbs": [ + "1  Introduction" + ] + }, + { + "objectID": "01-introduction.html#relationship-to-rmc-rfa", + "href": "01-introduction.html#relationship-to-rmc-rfa", + "title": "1  Introduction", + "section": "1.3 Relationship to RMC-RFA", + "text": "1.3 Relationship to RMC-RFA\nrfaR reimplements the core RMC-RFA methodology in open-source R, offering:\n\nReproducibility and transparency in the routing and sampling logic\nIntegration with the broader R/RMC ecosystem (e.g., RMC-BestFit outputs)", + "crumbs": [ + "1  Introduction" + ] + }, + { + "objectID": "01-introduction.html#how-this-manual-is-organized", + "href": "01-introduction.html#how-this-manual-is-organized", + "title": "1  Introduction", + "section": "1.4 How This Manual Is Organized", + "text": "1.4 How This Manual Is Organized\n\nMethodology — statistical and hydrologic methods behind the two-loop simulation framework\nInstallation — getting rfaR running locally\nWorkflow — an end-to-end walkthrough from raw inputs to a stage-frequency curve\nSimulation Modes — Median, Expected, and Full Uncertainty simulation modes explained\nFunction Reference — Function references (auto-generated from package documentation)\nValidation — comparison against RMC-RFA / HEC-HMS benchmark results\n\nFor function-level documentation, also see the pkgdown reference site.\n\n\n\n\n\n\nAven, Terje, Yakov Ben-Haim, Henning Boje Andersen, et al. n.d. Society for Risk Analysis Glossary.", + "crumbs": [ + "1  Introduction" + ] + }, + { + "objectID": "01-introduction.html#footnotes", + "href": "01-introduction.html#footnotes", + "title": "1  Introduction", + "section": "", + "text": "In risk analysis, the hazard is the risk source where potential consequences relate to harm (Aven et al., n.d.). In the context of dam and levee risk analyses, the source of risk is often defined using a reservoir stage-frequency relationship. The terms hydrologic loading, stage-frequency, and hydrologic hazard are used interchangeably to relate reservoir/river stages to annual exceedance probabilities (AEPs). These relationships are commonly presented as curves and are used as probabilistic loading conditions in dam and levee risk analyses.↩︎", + "crumbs": [ + "1  Introduction" + ] + }, + { + "objectID": "02-methodology.html", + "href": "02-methodology.html", + "title": "2  Methodology", + "section": "", + "text": "2.1 Overview\nrfaR implements a two-loop, nested Monte Carlo framework to propagate both natural variability and knowledge uncertainty through a deterministic flood routing model.", + "crumbs": [ + "2  Methodology" + ] + }, + { + "objectID": "02-methodology.html#outer-loop-parameter-uncertainty", + "href": "02-methodology.html#outer-loop-parameter-uncertainty", + "title": "2  Methodology", + "section": "2.2 Outer Loop: Parameter Uncertainty", + "text": "2.2 Outer Loop: Parameter Uncertainty", + "crumbs": [ + "2  Methodology" + ] + }, + { + "objectID": "02-methodology.html#inner-loop-realizations", + "href": "02-methodology.html#inner-loop-realizations", + "title": "2  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"text": "4.5 Step 4: Routing", + "crumbs": [ + "4  Workflow" + ] + }, + { + "objectID": "04-workflow.html#step-5-running-the-full-simulation", + "href": "04-workflow.html#step-5-running-the-full-simulation", + "title": "4  Workflow", + "section": "4.6 Step 5: Running the Full Simulation", + "text": "4.6 Step 5: Running the Full Simulation", + "crumbs": [ + "4  Workflow" + ] + }, + { + "objectID": "04-workflow.html#step-6-interpreting-results", + "href": "04-workflow.html#step-6-interpreting-results", + "title": "4  Workflow", + "section": "4.7 Step 6: Interpreting Results", + "text": "4.7 Step 6: Interpreting Results", + "crumbs": [ + "4  Workflow" + ] + }, + { + "objectID": "05-function-reference.html", + "href": "05-function-reference.html", + "title": "5  Function Reference", + "section": "", + "text": "5.1 Execute Reservoir Frequency Analysis", + "crumbs": [ + "5  Function Reference" + ] + }, + { + "objectID": "05-function-reference.html#execute-reservoir-frequency-analysis", + "href": "05-function-reference.html#execute-reservoir-frequency-analysis", + "title": "5  Function Reference", + "section": "", + "text": "5.1.1 rfa_simulate()\nRFA Stage-Frequency Simulation\n\nDescription:\n\n Performs reservoir stage frequency analysis using Monte Carlo\n simulation to develop stage-frequency relationships. Combines\n stratified flow-frequency sampling, flood seasonality, hydrograph\n scaling, and Modified Puls reservoir routing to estimate annual\n exceedance probabilities of reservoir stage.\n\nUsage:\n\n rfa_simulate(\n sim_type = \"expected\",\n bestfit_params,\n dist = \"LP3\",\n stage_ts,\n seasonality,\n hydrographs,\n resmodel,\n Nbins = 50,\n events_per_bin = 200,\n routing_dt = 1,\n Ncores = NULL,\n sim_name = NULL,\n results_dir = NULL\n )\n \nArguments:\n\nsim_type: Character string specifying the simulation mode. One of\n ‘\"median\"’, ‘\"expected\"’, or ‘\"full\"’. Default is\n ‘\"expected\"’.\n\nbestfit_params: Data frame or matrix of distribution parameters from\n RMC-BestFit MCMC output. Columns 1-3 are distribution\n parameters:\n\n * LP3: mean (log), standard deviation (log), skew (log)\n\n * GEV: location, scale, shape\n\n Column 4 is log-likelihood (used only in ‘\"median\"’ mode to\n identify the posterior mode). For ‘\"expected\"’ mode, must\n have at least ‘Nbins * events_per_bin’ rows. For ‘\"full\"’\n mode, each row is treated as an independent realization.\n\n dist: Character string specifying the frequency distribution.\n Either ‘\"LP3\"’ (Log-Pearson Type III, default) or ‘\"GEV\"’\n (Generalized Extreme Value).\n\nstage_ts: Data frame of historical reservoir stage with columns ‘date’\n (character in M/D/YYYY format) and ‘stage’ (numeric, feet).\n\nseasonality: Numeric vector of length 12 giving monthly flood\n occurrence probabilities (relative frequencies). Used to\n sample the month of each simulated event, which determines\n the antecedent pool elevation.\n\nhydrographs: List of hydrograph data frames as returned by\n ‘hydrograph_setup’. Each element has columns ‘datetime’,\n ‘hour’, ‘inflow’, and ‘hydrograph_num’, with attributes\n ‘\"obs_vol\"’ (observed max n-day volume) and ‘\"dt\"’ (timestep\n in hours). The list must have a ‘\"probs\"’ attribute\n containing normalized sampling probabilities.\n\nresmodel: Data frame with three columns: elevation (ft), storage\n (acre-ft), and discharge (cfs). Defines the reservoir model\n for Modified Puls routing.\n\n Nbins: Integer. Number of stratified sampling bins in EV1 space.\n Default is 50.\n\nevents_per_bin: Integer. Number of events sampled per bin. Default is\n 200. Total simulations per realization = ‘Nbins *\n events_per_bin’.\n\nrouting_dt: Numeric. Routing timestep in hours. Passed to\n ‘scale_hydrograph’ to resample hydrographs before routing.\n Supported values are ‘0.25’ (15-min), ‘1’ (1-hour, default),\n ‘6’ (6-hour), and ‘24’ (24-hour).\n\n Ncores: Integer or ‘NULL’. Number of parallel workers for ‘\"full\"’\n mode. If ‘NULL’ (default), automatically selects based on\n available cores (capped at 16). Ignored for ‘\"median\"’ and\n ‘\"expected\"’ modes.\n\nsim_name: Optional character string to label the simulation. Used in\n the output CSV filename. If ‘NULL’ (default), defaults to\n ‘\"sim\"’. Spaces are replaced with underscores in the\n filename.\n\nresults_dir: Optional path to the directory where results are saved. If\n ‘NULL’ (default), creates an ‘rfaR_results’ folder in the\n current working directory.\n\nDetails:\n\n Three simulation modes are available:\n\n ‘\"median\"’ Single realization using the most likely (posterior\n mode) parameter set from RMC-BestFit. Produces one\n stage-frequency curve with no uncertainty bounds. Useful for\n quick screening or debugging.\n\n ‘\"expected\"’ Single realization using expected value sampling,\n where each stratified flow sample is paired 1-to-1 with a\n different parameter set from the posterior distribution.\n Produces one stage-frequency curve representing the expected\n (mean) result integrated across parameter uncertainty.\n\n ‘\"full\"’ Nested Monte Carlo with full uncertainty quantification.\n The outer loop iterates over all parameter sets (one per row\n of ‘bestfit_params’), and each inner loop independently\n samples natural variability via stratified sampling,\n seasonality, starting pool, and hydrograph shape. Each\n realization produces an independent stage-frequency curve;\n curves are combined on a common stage grid to yield expected,\n median, and 5th/95th percentile confidence bounds. The outer\n loop is parallelized using the ‘future’ framework.\n\n For all modes, natural variability is captured through stratified\n sampling in with ‘Nbins’ bins and ‘events_per_bin’ events per bin,\n providing reliable coverage from common events down to\n approximately 1e-8 AEP.\n\n The stratified sampling approach divides the AEP range (default\n 0.99 to 1e-8) into equal-width bins in EV1 (Gumbel reduced\n variate) space and samples events uniformly within each bin. This\n ensures adequate representation of rare flood events that would\n require orders of magnitude more samples under crude Monte Carlo\n sampling.\n\n Post-processing uses the law of total probability via\n ‘stage_frequency_curve’: for a grid of stage thresholds, the\n weighted exceedance fraction is computed within each bin and\n summed across bins.\n\n In ‘\"full\"’ mode, each parallel worker independently samples all\n random inputs (seasonality, starting pool, hydrograph shape, and\n stratified z-variates) in addition to using its own parameter set.\n This ensures full Monte Carlo independence across realizations.\n Each worker post-processes its results into a stage-frequency\n curve immediately, returning only the curve rather than the full\n peak stage matrix, keeping memory usage manageable for large\n numbers of realizations.\n\n Curves from individual realizations are combined by interpolating\n each onto a common stage grid (spanning the global min/max across\n all realizations) in log10(AEP) space, then computing summary\n statistics across realizations at each stage threshold.\n\n Results are automatically exported as a CSV file named\n ‘{sim_name}_{sim_type}_{MM_DD_YY_HHMM}.csv’ in the ‘results_dir’\n directory. For example, a median simulation named \"John McGraw\n Dam\" run on June 4, 2025 at 2:30 PM would produce\n ‘John_McGraw_Dam_median_06_04_25_1430.csv’.\n\nValue:\n\n A list whose contents depend on ‘sim_type’:\n\n For ‘\"median\"’ and ‘\"expected\"’:\n\n stage_frequency Data frame with columns ‘stage’ and ‘AEP’ from\n ‘stage_frequency_curve’.\n\n peakStage Matrix of simulated peak stages (‘events_per_bin’ rows\n by ‘Nbins’ columns).\n\n peakFlow Matrix of simulated peak discharges (same dimensions).\n\n weights Stratified sampling bin weights.\n\n For ‘\"full\"’:\n\n stage_frequency Data frame with columns ‘stage’, ‘expected’,\n ‘median’, ‘lower_05’, and ‘upper_95’ on a common stage grid.\n\n all_curves List of per-realization stage-frequency data frames\n (each with ‘stage’ and ‘AEP’ columns).\n\n aep_matrix Matrix of interpolated AEP values (500 rows by\n ‘Nrealizations’ columns) on the common stage grid.\n\n common_stage Numeric vector of the common stage grid (length 500).\n\n Nrealizations Number of parameter set realizations processed.\n\n Nsims_per_realiz Number of simulations per realization.\n\nSee Also:\n\n ‘flow_frequency_sampler’, ‘flow_frequency_sampler_expected’,\n ‘stratified_sampler’, ‘scale_hydrograph’, ‘mod_puls_routing’,\n ‘stage_frequency_curve’, ‘hydrograph_setup’\n\nExamples:\n\n ## Not run:\n \n # Not run: Expected and Median modes take ~15s; Full uncert\n # runs ~10,000 parameter realizations and takes 3-4 hours on a\n # typical machine.\n \n # --- Setup ---\n hydros <- hydrograph_setup(jmd_hydro_apr1999, jmd_hydro_jun1965,\n jmd_hydro_may1955, jmd_hydro_pmf,\n critical_duration = 2, routing_days = 10)\n \n # --- Expected only (default) ---\n results_exp <- rfa_simulate(\n sim_type = \"expected\",\n bestfit_params = jmd_bf_parameter_sets,\n stage_ts = jmd_wy1980_stage,\n seasonality = jmd_seasonality$relative_frequency,\n hydrographs = hydros,\n resmodel = jmd_resmodel,\n sim_name = \"jmd\"\n )\n \n # --- Median only ---\n results_med <- rfa_simulate(\n sim_type = \"median\",\n bestfit_params = jmd_bf_parameter_sets,\n stage_ts = jmd_wy1980_stage,\n seasonality = jmd_seasonality$relative_frequency,\n hydrographs = hydros,\n resmodel = jmd_resmodel,\n sim_name = \"jmd\"\n )\n \n # --- Full uncertainty (parallelized) ---\n results_full <- rfa_simulate(\n sim_type = \"full\",\n bestfit_params = jmd_bf_parameter_sets,\n stage_ts = jmd_wy1980_stage,\n seasonality = jmd_seasonality$relative_frequency,\n hydrographs = hydros,\n resmodel = jmd_resmodel,\n Ncores = 4,\n sim_name = \"jmd\"\n )\n ## End(Not run)", + "crumbs": [ + "5  Function Reference" + ] + }, + { + "objectID": "05-function-reference.html#primary-rfa-modules", + "href": "05-function-reference.html#primary-rfa-modules", + "title": "5  Function Reference", + "section": "5.2 Primary RFA Modules", + "text": "5.2 Primary RFA Modules\n\n5.2.1 stratified_sampler()\nStratified Sampler\n\nDescription:\n\n Creates stratified bins in standard normal space for use in\n stratified sampling. Stratification improves sampling efficiency\n by ensuring adequate coverage of rare events.\n\nUsage:\n\n stratified_sampler(\n minAEP = 1e-08,\n maxAEP = 0.99,\n dist = \"EV1\",\n Nbins = NULL,\n Mevents = NULL,\n verbose = FALSE\n )\n \nArguments:\n\n minAEP: Minimum annual exceedance probability. Default is ‘1E-8’.\n\n maxAEP: Maximum annual exceedance probability. Default is ‘0.99’.\n\n dist: Character. Probability space for stratification.\n Case-insensitive. Default is ‘\"EV1\"’. Invalid values trigger\n a warning and default to ‘\"EV1\"’.\n\n ‘\"EV1\"’ Extreme Value Type I (Gumbel) space. Recommended for\n flood frequency analysis. Allocates more bins to rare\n events through the transformation ‘-log(-log(1-AEP))’,\n improving tail estimation efficiency.\n\n ‘\"Normal\"’ Standard normal (z-score) space. Uniform bins in\n z-space. Use when the underlying phenomenon is normally\n distributed.\n\n ‘\"Uniform\"’ Uniform probability space. Equal probability\n width per bin. Generally inefficient for rare event\n estimation.\n\n Nbins: Number of stratified bins. Default is ‘20’.\n\n Mevents: Number of events per bin. Default is ‘500’.\n\n verbose: Logical. If ‘TRUE’, prints a completion message summarizing\n the stratification. Default is ‘FALSE’.\n\nDetails:\n\n The function divides the probability space into bins using the\n specified transformation. EV1 transformation is recommended for\n heavy-tailed distributions common in flood frequency analysis, as\n it naturally allocates more sampling effort to rare events\n critical for dam safety assessments.\n\nValue:\n\n A list containing:\n\n normOrd Vector of standard normal ordinates spanning all bins\n\n Weights Vector of probability weights for each bin\n\n Zlower Vector of lower bounds for each bin (standard normal)\n\n Zupper Vector of upper bounds for each bin (standard normal)\n\n Nbins Number of bins\n\n Mevents Number of events per bin\n\nExamples:\n\n # Default stratification\n strat <- stratified_sampler()\n \n # Custom bins and events\n strat <- stratified_sampler(minAEP = 1E-6,\n maxAEP = 0.5,\n dist = \"EV1\",\n Nbins = 10,\n Mevents = 100)\n \n # With verbose message\n strat <- stratified_sampler(verbose = TRUE)\n \n\n\n5.2.2 flow_frequency_sampler()\nFlow Frequency Sampler\n\nDescription:\n\n Generates a stratified matrix of flow values from a single set of\n frequency distribution parameters using stratified Monte Carlo\n sampling. Used internally by ‘rfa_simulate()’ for median-only and\n full uncertainty modes.\n\nUsage:\n\n flow_frequency_sampler(\n bestfit_params,\n freq_dist = \"LP3\",\n strat_dist = \"ev1\",\n Nbin = NULL,\n Mevent = NULL\n )\n \nArguments:\n\nbestfit_params: Numeric vector of length 3 containing distribution\n parameters. For LP3: ‘c(mean_log, sd_log, skew_log)’. For\n GEV: ‘c(location, scale, shape)’.\n\nfreq_dist: Character. Distribution type. Either ‘\"LP3\"’ (default) or\n ‘\"GEV\"’.\n\nstrat_dist: Character. Probability space for stratification bins.\n Passed to ‘stratified_sampler()’. One of ‘\"ev1\"’ (default),\n ‘\"normal\"’, or ‘\"uniform\"’. See ‘stratified_sampler()’ for\n details.\n\n Nbin: Integer. Number of stratified bins. Default is ‘50’.\n\n Mevent: Integer. Number of events per bin. Default is ‘200’.\n\nValue:\n\n A list containing:\n\n flow Matrix of sampled flow values [Mevent x Nbin]\n\n nbins Number of stratified bins\n\n mevents Number of events per bin\n\n weights Probability weights for each bin from\n ‘stratified_sampler()’\n\nSee Also:\n\n ‘stratified_sampler()’, ‘qp3()’, ‘rfa_simulate()’\n\nExamples:\n\n # Single parameter set (posterior mode)\n params <- c(4.85, 0.39, -0.15)\n result <- flow_frequency_sampler(params, freq_dist = \"LP3\",\n Nbin = 20, Mevent = 500)\n # Dimensions of result\n dim(result$flow) # 500 x 20\n \n # Distribution of Sampled Flows\n hist(result$flow)\n \n\n\n5.2.3 flow_frequency_sampler_expected()\nFlow Frequency Sampler (Expected Only)\n\nDescription:\n\n Generates a stratified matrix of flow values by pairing each\n z-ordinate with a different parameter set from the posterior\n distribution. This collapses the nested Monte Carlo structure into\n a single pass, simultaneously sampling natural variability (via\n stratified z-ordinates) and knowledge uncertainty (via varying\n parameters). Used internally by ‘rfa_simulate()’ for expected-only\n mode.\n\nUsage:\n\n flow_frequency_sampler_expected(\n bestfit_params,\n freq_dist = \"LP3\",\n strat_dist = \"ev1\",\n Nbin = NULL,\n Mevent = NULL\n )\n \nArguments:\n\nbestfit_params: Data frame of distribution parameters from RMC-BestFit.\n Must have ‘Nbin * Mevent’ rows (one parameter set per\n z-ordinate). For LP3: columns are mean (log), sd (log), skew\n (log). For GEV: columns are location, scale, shape.\n\nfreq_dist: Character. Distribution type. Either ‘\"LP3\"’ (default) or\n ‘\"GEV\"’.\n\nstrat_dist: Character. Probability space for stratification bins.\n Passed to ‘stratified_sampler()’. One of ‘\"ev1\"’ (default),\n ‘\"normal\"’, or ‘\"uniform\"’. See ‘stratified_sampler()’ for\n details.\n\n Nbin: Integer. Number of stratified bins. Default is ‘50’.\n\n Mevent: Integer. Number of events per bin. Default is ‘200’.\n\nValue:\n\n A list containing:\n\n flow Matrix of sampled flow values [Mevent x Nbin]\n\n nbins Number of stratified bins\n\n mevents Number of events per bin\n\n weights Probability weights for each bin from\n ‘stratified_sampler()’\n\nSee Also:\n\n ‘stratified_sampler()’, ‘qp3()’, ‘flow_frequency_sampler()’,\n ‘rfa_simulate()’\n\nExamples:\n\n # Using a pre-loaded parameter set (all 10,000 parameter sets)\n result <- flow_frequency_sampler_expected(jmd_bf_parameter_sets,\n Nbin = 20, Mevent = 500)\n dim(result$flow) # 500 x 20\n \n # Using bootstrapped parameter samples\n jmd_samples <- bootstrap_vfc(\n c(jmd_vfc_parameters$mean_log,\n jmd_vfc_parameters$sd_log,\n jmd_vfc_parameters$skew_log),\n dist = \"LP3\",\n ERL = jmd_vfc_parameters$erl)\n \n jmd_result <- flow_frequency_sampler_expected(\n jmd_samples$params,\n freq_dist = \"LP3\",\n Nbin = 20,\n Mevent = 500)\n \n\n\n5.2.4 hydrograph_setup()\nHydrograph Setup for RFA Simulation\n\nDescription:\n\n Prepares hydrograph data frames copied from RMC-RFA for use in RFA\n simulation. Converts date/time columns and adds sequential hour\n and hydrograph ID columns.\n\nUsage:\n\n hydrograph_setup(\n ...,\n critical_duration = NULL,\n routing_days = NULL,\n weights = NULL\n )\n \nArguments:\n\n ...: Data frame representing an input hydrograph with columns:\n Ord, Date, Time, Flow (cfs). Copied directly from RMC-RFA.\n Date & Time should be ‘class() = \"character\"’\n\ncritical_duration: Critical duration in days.\n\nrouting_days: Desired length of routing simulation in days.\n\n weights: Optional numeric vector of sampling weights for each\n hydrograph. Must be the same length as the number of input\n hydrographs. Weights are normalized to probabilities\n internally. If NULL (default), all hydrographs are weighted\n equally.\n\nValue:\n\n A list of formatted hydrograph data frames, each containing:\n\n datetime Date-time (POSIXct)\n\n hour Hours from start of event\n\n inflow Inflow (cfs)\n\n hydrograph_num Hydrograph ID number for sampling\n\n obs_vol Max n-day inflow volume (stored as an attribute of each\n dataframe)\n\n dt Hydrograph timestep (delta time, dt) in hours (stored as an\n attribute of each dataframe)\n\n The returned list also has a ‘probs’ attribute containing the\n normalized sampling probabilities derived from ‘weights’.\n\nExamples:\n\n # Setup with equal weights (default)\n hydros <- hydrograph_setup(jmd_hydro_apr1999,\n jmd_hydro_jun1965,\n jmd_hydro_pmf,\n critical_duration = 2,\n routing_days = 10)\n \n # Setup with custom weights (PMF 3x more likely to be sampled)\n hydros <- hydrograph_setup(jmd_hydro_apr1999,\n jmd_hydro_jun1965,\n jmd_hydro_pmf,\n critical_duration = 2,\n routing_days = 10,\n weights = c(1, 1, 3))\n \n # view normalized probabilities\n attr(hydros, \"probs\")\n \n\n\n5.2.5 scale_hydrograph()\nScale Hydrograph\n\nDescription:\n\n Scales inflow hydrograph given a sampled inflow volume from the\n volume-frequency curve. Scale factor is defined by the sampled\n inflow volume and the maximum volume of the corresponding\n duration. If the native hydrograph timestep differs from the\n target routing timestep, resampling is applied prior to scaling:\n finer-to-coarser uses block averaging; coarser-to-finer uses\n linear interpolation.\n\nUsage:\n\n scale_hydrograph(\n hydrograph_shape,\n observed_volume,\n sampled_volume,\n routing_dt = 1\n )\n \nArguments:\n\nhydrograph_shape: Data frame with two columns: time (hours) and inflow\n (cfs). Must be in that order.\n\nobserved_volume: Maximum n-day inflow volume from input hydrograph\n shape.\n\nsampled_volume: Sampled inflow volume from volume-frequency curve.\n\nrouting_dt: Target routing timestep in hours. Supported values are\n ‘0.25’ (15-min), ‘1’ (1-hour, default), ‘6’ (6-hour), and\n ‘24’ (24-hour).\n\nValue:\n\n A data frame with two columns: ‘time_hrs’ and ‘inflow_cfs’, at the\n target routing timestep, with time starting at hour 0.\n\nExamples:\n\n # Example hydrograph. Requires pre-processing\n hydro_example <- hydrograph_setup(jmd_hydro_jun1965_15min, critical_duration = 2, routing_days = 10)\n hydrograph_shape <- hydro_example[[1]][, 2:3]\n \n # Default 1-hour routing timestep\n scaled <- scale_hydrograph(hydrograph_shape,\n observed_volume = 50000,\n sampled_volume = 55000)\n \n # 15-min input hydrograph, 1-hour routing timestep\n scaled <- scale_hydrograph(hydrograph_shape,\n observed_volume = 50000,\n sampled_volume = 55000,\n routing_dt = 1)\n \n\n\n5.2.6 mod_puls_routing()\nModified Puls Reservoir Routing\n\nDescription:\n\n Performs Modified Puls (level pool) routing of inflow hydrograph\n given a defined reservoir geometry (Stage (ft), Storage (ac-ft),\n Discharge (cfs)).\n\nUsage:\n\n mod_puls_routing(resmodel_df, inflow_df, initial_elev, full_results = FALSE)\n \nArguments:\n\nresmodel_df: Data frame with three columns: elevation/stage (ft),\n storage (acre-feet), and discharge (cfs). Must be in that\n order.\n\ninflow_df: Data frame with two columns: time (hours) and inflow (cfs).\n Must be in that order.\n\ninitial_elev: Starting water surface elevation in feet.\n\nfull_results: Logical. If ‘FALSE’ (default), returns only peak stage\n and discharge. If ‘TRUE’, returns the complete routing\n result.\n\nValue:\n\n If ‘full_results = FALSE’, a named numeric vector with\n ‘peak_stage_ft’ and ‘peak_discharge_cfs’. If ‘full_results =\n TRUE’, a data frame with columns: ‘time_hr’, ‘inflow_cfs’,\n ‘elevation_ft’, ‘storage_acft’, and ‘outflow_cfs’.\n\nReferences:\n\n Chow, V.T. (1959). Open-Channel Hydraulics. McGraw-Hill.\n\nExamples:\n\n # Example hydrograph. Requires pre-processing\n hydro_example <- hydrograph_setup(jmd_hydro_jun1965_15min,\n critical_duration = 2,\n routing_days = 10)\n hydrograph_shape <- hydro_example[[1]][, 2:3]\n scaled_hydrograph <- scale_hydrograph(hydrograph_shape,\n observed_volume = 50000,\n sampled_volume = 55000)\n \n # Peak values only\n mod_puls_routing(jmd_resmodel, scaled_hydrograph, initial_elev = 3830)\n \n # Full routing table\n jmd_full_routing <- mod_puls_routing(jmd_resmodel,\n scaled_hydrograph,\n initial_elev = 3830,\n full_results = TRUE)\n head(jmd_full_routing)\n \n\n\n5.2.7 stage_frequency_curve()\nCompute Stage-Frequency Curve\n\nDescription:\n\n Converts a matrix of routed peak stages into a stage-frequency\n curve using the law of total probability across stratified bins.\n For each candidate stage, the conditional exceedance probability\n within each bin is weighted by the bin probability and summed to\n produce the unconditional annual exceedance probability (AEP).\n\nUsage:\n\n stage_frequency_curve(peakStage, weights, stage_bins = 1000)\n \nArguments:\n\npeakStage: Matrix of peak stages [Mevents x Nbins] from routing.\n\n weights: Numeric vector of bin weights from ‘stratified_sampler()’.\n Must have length equal to ‘ncol(peakStage)’.\n\nstage_bins: Numeric value of exceedance stages used to compute\n stage-frequency in each bin. Default is 1000.\n\nValue:\n\n A data frame with columns:\n\n stage Evaluated stage values\n\n AEP Annual exceedance probability at each stage\n\nSee Also:\n\n ‘stratified_sampler()’, ‘flow_frequency_sampler()’,\n ‘rfa_simulate()’", + "crumbs": [ + "5  Function Reference" + ] + }, + { + "objectID": "05-function-reference.html#rejection-sampling", + "href": "05-function-reference.html#rejection-sampling", + "title": "5  Function Reference", + "section": "5.3 Rejection Sampling", + "text": "5.3 Rejection Sampling\n\n5.3.1 pmf_stage_lognormal()\nParameterize a Three-Parameter Lognormal PMF for Stage\n\nDescription:\n\n Computes the parameters of a three-parameter lognormal\n distribution for use as a probabilistic maximum stage (PMF) in\n rejection sampling. The shift parameter defines the lower or upper\n bound of the distribution (typically, the lower value). The\n function supports two modes: (1) supplying a best estimate and\n assuming sigma, or (2) supplying a best estimate, low, and high to\n solve for sigma numerically.\n\nUsage:\n\n pmf_stage_lognormal(\n pmf_shift,\n pmf_mean,\n pmf_sigma = NULL,\n pmf_low = NULL,\n pmf_high = NULL\n )\n \nArguments:\n\npmf_shift: Numeric. Assumed lower-bound of the lognormal distribution.\n This will define the shift value. Must be less than all of\n ‘pmf_mean’, ‘pmf_low’, and ‘pmf_high’.\n\npmf_mean: Numeric. Assumed mean of the shifted PMF stage distribution.\n\npmf_sigma: Numeric. Assumed standard deviation on the log scale.\n Required if ‘pmf_low’ and ‘pmf_high’ are not supplied.\n Defaults to ‘NULL’. Suggested value = 0.5.\n\n pmf_low: Numeric. Low estimate of PMF stage (assumed 5th percentile).\n Optional. If supplied, ‘pmf_high’ must also be supplied.\n\npmf_high: Numeric. High estimate of PMF stage (assumed 95th\n percentile). Optional. If supplied, ‘pmf_low’ must also be\n supplied.\n\nValue:\n\n A named list with the following elements:\n\n pmf_shift Hard lower bound of the distribution.\n\n pmf_mean Assumed mean of shifted PMF stage distribution.\n\n pmf_sigma Standard deviation on the log scale.\n\n pmf_mu Derived location parameter on the log scale.\n\n pmf_p05 5th percentile of the PMF stage distribution.\n\n pmf_p95 95th percentile of the PMF stage distribution.\n\nExamples:\n\n # Mode 1: assume sigma\n pmf <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, pmf_sigma = 0.5)\n \n # Mode 2: solve for sigma from low/high estimates\n pmf <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9,\n pmf_low = 239, pmf_high = 245)\n \n\n\n5.3.2 rejection_sampling_stage()\nRejection Sampling of Stage Bounded by a Probabilistic Maximum Stage\n\nDescription:\n\n Draws ‘n_samples’ stage values from a stage-frequency curve,\n rejecting any draws that exceed a probabilistic maximum stage\n (PMF) sampled from a three-parameter lognormal distribution.\n Accepted samples are ranked and assigned Weibull plotting\n positions for use in frequency analysis.\n\nUsage:\n\n rejection_sampling_stage(\n pmf_stage_LN,\n stage_freq_df = NULL,\n aep = NULL,\n stage = NULL,\n n_samples = 1e+07\n )\n \nArguments:\n\npmf_stage_LN: A named list returned by ‘pmf_stage_lognormal’ containing\n the lognormal PMF parameters.\n\nstage_freq_df: A data frame with AEP in column 1 and stage in column 2.\n Mutually exclusive with ‘aep’ and ‘stage’.\n\n aep: Numeric vector of annual exceedance probabilities. Must be\n supplied with ‘stage’. Mutually exclusive with\n ‘stage_freq_df’.\n\n stage: Numeric vector of stages corresponding to ‘aep’. Must be\n supplied with ‘aep’. Mutually exclusive with ‘stage_freq_df’.\n\nn_samples: Integer. Number of samples to draw. Defaults to ‘1e7’.\n\nValue:\n\n A data frame of thinned accepted samples with columns for z-score\n and stage, suitable for plotting a probabilistically bounded\n stage-frequency curve. Output is produced by ‘thin_samples()’.\n\nSee Also:\n\n ‘pmf_stage_lognormal’\n\nExamples:\n\n ## Not run:\n \n pmf_ln <- pmf_stage_lognormal(pmf_shift = 239, pmf_mean = 241.9, pmf_sigma = 0.5)\n \n result <- rejection_sampling_stage(\n pmf_stage_LN = pmf_ln,\n stage_freq_df = my_stage_freq_df,\n n_samples = 1e7\n )\n ## End(Not run)", + "crumbs": [ + "5  Function Reference" + ] + }, + { + "objectID": "05-function-reference.html#additional-utilities", + "href": "05-function-reference.html#additional-utilities", + "title": "5  Function Reference", + "section": "5.4 Additional Utilities", + "text": "5.4 Additional Utilities\n\n5.4.1 bootstrap_vfc()\nBootstrap option for VFC (RFA-style w/ ERL)\n\nDescription:\n\n Generates matrix of parameters using posterior mode/mean and ERL\n\nUsage:\n\n bootstrap_vfc(bestfit_postmode, dist = \"LP3\", ERL = 150, Nboots = 10000)\n \nArguments:\n\nbestfit_postmode: Vector of distribution parameters from RMC-BestFit.\n For LP3: columns are mean (log), sd (log), skew (log). For\n GEV: columns are location, scale, shape.\n\n dist: Distribution type. Either ‘\"LP3\"’ (default) or ‘\"GEV\"’.\n\n ERL: psuedo effective record length for bootstrapping\n\n Nboots: number of bootstraps\n\nValue:\n\n A list containing:\n\n params Matrix of bootstrapped parameters\n\n dist Distribution selected (LP3 or GEV)\n\n postmode Posterior mode parameters provided to bootstrap\n\n ERL pseudo effective record length provided to bootstrap\n\nSee Also:\n\n ‘flow_frequency_sampler()’\n\nExamples:\n\n # Sample using JMD VFC parameters\n jmd_samples <- bootstrap_vfc(c(jmd_vfc_parameters$mean_log,\n jmd_vfc_parameters$sd_log,\n jmd_vfc_parameters$skew_log),\n dist = \"LP3\",\n ERL = jmd_vfc_parameters$erl)\n \n # GEV Example with parent distribution parameters\n gev_example <- c(3.0, 1.0, -0.1)\n gev_samples <- bootstrap_vfc(gev_example,\n dist = \"GEV\",\n ERL = 200,\n Nboots = 5000)\n hist(gev_samples$params[,1])\n \n # LP3 Example with parent distribution parameters\n lp3_example <- c(3.5, 0.22, 0.1)\n lp3_samples <- bootstrap_vfc(lp3_example,\n dist = \"LP3\",\n ERL = 300,\n Nboots = 1000)\n hist(lp3_samples$params[,3])\n \n\n\n5.4.2 qp3()\nPearson Type III Inverse CDF (Quantile Function)\n\nDescription:\n\n Computes quantiles from a Pearson Type III distribution given\n probabilities and distribution parameters (mean, standard\n deviation, skewness).\n\nUsage:\n\n qp3(p, mu, sigma, gamma)\n \nArguments:\n\n p: Vector of probabilities (between 0 and 1).\n\n mu: Mean of the distribution.\n\n sigma: Standard deviation of the distribution.\n\n gamma: Skewness coefficient of the distribution.\n\nDetails:\n\n NOTE: This is the non-vectorized version of the function (it will\n be used in a loop) Vectorized version is part of future\n development.\n\n The Pearson Type III distribution is parameterized by mean (‘mu’),\n standard deviation (‘sigma’), and skewness (‘gamma’). These are\n converted internally to location, scale, and shape parameters.\n\n When skewness is near zero (‘abs(gamma) < 1E-3’), the normal\n distribution is used as an approximation.\n\nValue:\n\n Vector of quantiles corresponding to the input probabilities.\n\nExamples:\n\n # Single quantile\n qp3(0.99, mu = 10, sigma = 2, gamma = 0.5)\n \n # Multiple quantiles\n qp3(c(0.5, 0.9, 0.99), mu = 10, sigma = 2, gamma = 0.5)\n \n # Use with JMD volume-frequency parameters\n qp3(0.99,\n mu = jmd_vfc_parameters$mean_log,\n sigma = jmd_vfc_parameters$sd_log,\n gamma = jmd_vfc_parameters$skew_log)\n \n\n\n5.4.3 aep2stage()\nInterpolate Stage from AEP\n\nDescription:\n\n Given a stage-frequency curve, interpolates stage values at target\n AEPs. Interpolation is performed in standard normal (z-variate)\n space for better behavior across orders of magnitude in AEP.\n\nUsage:\n\n aep2stage(aep, stage, interp_aep)\n \nArguments:\n\n aep: Numeric vector of annual exceedance probabilities from the\n known curve.\n\n stage: Numeric vector of stage values corresponding to ‘aep’.\n\ninterp_aep: Numeric vector of target AEPs to interpolate at.\n\nValue:\n\n Numeric vector of interpolated stage values (same length as\n ‘interp_aep’).\n\nSee Also:\n\n ‘stage2aep’ for the inverse operation.\n\nExamples:\n\n # Get stage at the 1.12% and 0.175% AEP\n aep2stage(jmd_rfa_expected$AEP, jmd_rfa_expected$Expected, c(0.0112,1.75E-3))\n \n\n\n5.4.4 stage2aep()\nInterpolate AEP from Stage\n\nDescription:\n\n Given a stage-frequency curve, interpolates AEP values at target\n stages. Interpolation is performed in standard normal (z-variate)\n space for better behavior across orders of magnitude in AEP.\n\nUsage:\n\n stage2aep(aep, stage, interp_stage)\n \nArguments:\n\n aep: Numeric vector of annual exceedance probabilities from the\n known curve.\n\n stage: Numeric vector of stage values corresponding to ‘aep’.\n\ninterp_stage: Numeric vector of target stage values to interpolate at.\n\nDetails:\n\n Stage-frequency curves typically flatten at very rare AEPs once\n the reservoir reaches the reservoir model discharge capacity. This\n produces tied stage values. ‘approx()’ handles these by averaging\n the corresponding AEPs, which may emit a \"collapsing to unique 'x'\n values\" warning. This is expected behavior.\n\nValue:\n\n Numeric vector of interpolated AEP values (same length as\n ‘interp_stage’).\n\nSee Also:\n\n ‘aep2stage’ for the inverse operation.\n\nExamples:\n\n # Get AEP at stage 3862.0-ft and 3872-ft\n stage2aep(jmd_rfa_expected$AEP, jmd_rfa_expected$Expected, c(3862.0, 3872))\n \n\n\n5.4.5 interpolate_aep_matrix()\nInterpolate AEPs at Target Stages Across Realizations\n\nDescription:\n\n For each realization's stage-frequency curve, interpolates the AEP\n at every target stage value. Interpolation is performed in\n standard normal (z-variate) space.\n\nUsage:\n\n interpolate_aep_matrix(results_list, target_stages)\n \nArguments:\n\nresults_list: List of realization data frames, each with columns\n ‘stage’ and ‘AEP’.\n\ntarget_stages: Numeric vector of stage values to interpolate at.\n\nValue:\n\n Matrix of AEP values with rows = target stages and columns =\n realizations.\n\n\n\n5.4.6 interpolate_stage_matrix()\nInterpolate Stages at Target AEPs Across Realizations\n\nDescription:\n\n For each realization's stage-frequency curve, interpolates the\n stage at every target AEP value. Interpolation is performed in\n standard normal (z-variate) space.\n\nUsage:\n\n interpolate_stage_matrix(results_list, target_aeps)\n \nArguments:\n\nresults_list: List of realization data frames, each with columns\n ‘stage’ and ‘AEP’.\n\ntarget_aeps: Numeric vector of AEP values to interpolate at.\n\nValue:\n\n Matrix of stage values with rows = target AEPs and columns =\n realizations.\n\n\n\n5.4.7 power_function()\nPower Function\n\nDescription:\n\n Raises 10 to the 1st through the nth power\n\nUsage:\n\n power_function(x)\n \nArguments:\n\n x: Highest power of 10 to use\n\nValue:\n\n Vector of 10 raised sequentially from the 1st to the nth power.\n\n\n\n5.4.8 theme_rfar_conceptual()\nrfaR ggplot2 Theme for Conceptual Plots\n\nDescription:\n\n A custom ‘ggplot2’ theme used in the rfaR conceptual realization\n vignette. Based on ‘theme_bw’ with tightened text sizes, italic\n titles, and the legend suppressed. Intended for figures that\n emphasize curves over annotation density.\n\nUsage:\n\n theme_rfar_conceptual()\n \nValue:\n\n A ‘ggplot2’ theme object that can be added to a ‘ggplot’ via ‘+’.\n\nSee Also:\n\n ‘theme_bw’\n\nExamples:\n\n library(ggplot2)\n ggplot(jmd_rfa_expected, aes(x = AEP, y = Expected)) +\n geom_line() +\n scale_x_continuous(transform = c(\"log10\", \"reverse\")) +\n labs(title = \"JMD Expected Stage-Frequency\",\n subtitle = \"Demonstrating theme_rfar_conceptual()\",\n x = \"AEP\", y = \"Stage (ft)\") +\n theme_rfar_conceptual()", + "crumbs": [ + "5  Function Reference" + ] + }, + { + "objectID": "06-simulation-modes.html", + "href": "06-simulation-modes.html", + "title": "6  Simulation Modes", + "section": "", + "text": "6.1 Median Mode\nrfaR supports three simulation modes, corresponding to different treatments of parameter uncertainty.", + "crumbs": [ + "6  Simulation Modes" + ] + }, + { + "objectID": "06-simulation-modes.html#expected-mode", + "href": "06-simulation-modes.html#expected-mode", + "title": "6  Simulation Modes", + "section": "6.2 Expected Mode", + "text": "6.2 Expected Mode", + "crumbs": [ + "6  Simulation Modes" + ] + }, + { + "objectID": "06-simulation-modes.html#full-uncertainty-mode", + "href": "06-simulation-modes.html#full-uncertainty-mode", + "title": "6  Simulation Modes", + "section": "6.3 Full Uncertainty Mode", + "text": "6.3 Full Uncertainty Mode\nRuns the full nested Monte Carlo framework, propagating both parameter (epistemic) and event (aleatory) uncertainty.", + "crumbs": [ + "6  Simulation Modes" + ] + }, + { + "objectID": "06-simulation-modes.html#selecting-a-mode", + "href": "06-simulation-modes.html#selecting-a-mode", + "title": "6  Simulation Modes", + "section": "6.4 Selecting a Mode", + "text": "6.4 Selecting a Mode", + "crumbs": [ + "6  Simulation Modes" + ] + }, + { + "objectID": "07-validation.html", + "href": "07-validation.html", + "title": "7  Validation", + "section": "", + "text": "7.1 Purpose", + "crumbs": [ + "7  Validation" + ] + }, + { + "objectID": "07-validation.html#modified-puls-routing", + "href": "07-validation.html#modified-puls-routing", + "title": "7  Validation", + "section": "7.2 Modified Puls Routing", + "text": "7.2 Modified Puls Routing", + "crumbs": [ + "7  Validation" + ] + }, + { + "objectID": "07-validation.html#stratified-sampling", + "href": "07-validation.html#stratified-sampling", + "title": "7  Validation", + "section": "7.3 Stratified Sampling", + "text": "7.3 Stratified Sampling", + "crumbs": [ + "7  Validation" + ] + }, + { + "objectID": "07-validation.html#see-also", + "href": "07-validation.html#see-also", + "title": "7  Validation", + "section": "7.4 See Also", + "text": "7.4 See Also\nDetailed validation vignettes are also available on the pkgdown site.", + "crumbs": [ + "7  Validation" + ] + } +] \ No newline at end of file diff --git a/rfaR-book/_book/site_libs/bootstrap/bootstrap-9aacbc0ffca7e7247307b8a4340a139e.min.css 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media(min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: rgb(204.36, 208.68, 213);--bs-navbar-hover-color: rgba(255, 255, 255, 0.8);--bs-navbar-disabled-color: rgba(204.36, 208.68, 213, 0.75);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: rgb(204.36, 208.68, 213);--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(204.36, 208.68, 213, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgb%28204.36, 208.68, 213%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgb%28204.36, 208.68, 213%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: 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var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #212529;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #212529;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%2817.6, 24.8, 32%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: rgb(149.5, 158.5, 167.5);--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: rgb(17.6, 24.8, 32);--bs-accordion-active-bg: rgb(212.8, 216.4, 220)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28128.4, 139.2, 150%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28128.4, 139.2, 150%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #18bc9c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: rgb(15.3396226415, 120.1603773585, 99.7075471698);--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: rgb(19.2, 150.4, 124.8);--bs-pagination-focus-bg: #ecf0f1;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: rgb(15.3396226415, 120.1603773585, 99.7075471698);--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #ecf0f1;--bs-pagination-disabled-bg: rgb(58.5754716981, 229.9245283019, 196.4905660377);--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: 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.page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) 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var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #ecf0f1;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2c3e50;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #ecf0f1;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #ecf0f1;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ecf0f1;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2c3e50;--bs-list-group-active-border-color: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(44, 62, 80, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto 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70.4, 92);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e74c3c var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(231.4,70.4,92);color:#fff}.bg-gradient-red-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(239.8, 96, 44);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e74c3c var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) rgb(239.8,96,44);color:#fff}.bg-gradient-red-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(235.8, 108, 43.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e74c3c var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) rgb(235.8,108,43.2);color:#fff}.bg-gradient-red-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(148.2, 120.8, 98.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e74c3c var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) rgb(148.2,120.8,98.4);color:#fff}.bg-gradient-red-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(151.4, 126, 96.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e74c3c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(151.4,126,96.4);color:#fff}.bg-gradient-red-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(159.4, 106.4, 123.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #e74c3c var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) rgb(159.4,106.4,123.6);color:#fff}.bg-gradient-orange-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(169.4, 100.4, 44);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #2c3e50 var(--bg-gradient-end, 180%)) rgb(169.4,100.4,44);color:#fff}.bg-gradient-orange-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(192.6, 82, 108.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) 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var(--bg-gradient-end, 180%)) rgb(249,138,19.2);color:#fff}.bg-gradient-orange-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(161.4, 150.8, 74.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) rgb(161.4,150.8,74.4);color:#fff}.bg-gradient-orange-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(164.6, 156, 72.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(164.6,156,72.4);color:#fff}.bg-gradient-orange-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(172.6, 136.4, 99.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) rgb(172.6,136.4,99.6);color:#fff}.bg-gradient-yellow-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(163.4, 118.4, 42.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 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34.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) rgb(238.2,124,34.8);color:#fff}.bg-gradient-yellow-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(247, 144, 18.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) rgb(247,144,18.8);color:#fff}.bg-gradient-yellow-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(155.4, 168.8, 73.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) rgb(155.4,168.8,73.2);color:#fff}.bg-gradient-yellow-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(158.6, 174, 71.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(158.6,174,71.2);color:#fff}.bg-gradient-yellow-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(166.6, 154.4, 98.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) rgb(166.6,154.4,98.4);color:#fff}.bg-gradient-green-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(32, 137.6, 125.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #18bc9c var(--bg-gradient-start, 36%), #2c3e50 var(--bg-gradient-end, 180%)) rgb(32,137.6,125.6);color:#fff}.bg-gradient-green-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(55.2, 119.2, 190.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #18bc9c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(55.2,119.2,190.4);color:#fff}.bg-gradient-green-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(58.8, 139.2, 170.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #18bc9c var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) rgb(58.8,139.2,170.8);color:#fff}.bg-gradient-green-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(107.2, 137.6, 149.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #18bc9c var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(107.2,137.6,149.6);color:#fff}.bg-gradient-green-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(106.8, 143.2, 117.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #18bc9c var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) rgb(106.8,143.2,117.6);color:#fff}.bg-gradient-green-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(115.6, 163.2, 101.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #18bc9c var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) rgb(115.6,163.2,101.6);color:#fff}.bg-gradient-green-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(111.6, 175.2, 100.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #18bc9c var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) rgb(111.6,175.2,100.8);color:#fff}.bg-gradient-green-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(27.2, 193.2, 154);background:linear-gradient(var(--bg-gradient-deg, 140deg), #18bc9c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(27.2,193.2,154);color:#fff}.bg-gradient-green-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(35.2, 173.6, 181.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #18bc9c var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) rgb(35.2,173.6,181.2);color:#fff}.bg-gradient-teal-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(36.8, 145.4, 122.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #2c3e50 var(--bg-gradient-end, 180%)) rgb(36.8,145.4,122.6);color:#fff}.bg-gradient-teal-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(60, 127, 187.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(60,127,187.4);color:#fff}.bg-gradient-teal-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(63.6, 147, 167.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) rgb(63.6,147,167.8);color:#fff}.bg-gradient-teal-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(112, 145.4, 146.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(112,145.4,146.6);color:#fff}.bg-gradient-teal-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(111.6, 151, 114.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) rgb(111.6,151,114.6);color:#fff}.bg-gradient-teal-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(120.4, 171, 98.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) rgb(120.4,171,98.6);color:#fff}.bg-gradient-teal-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(116.4, 183, 97.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) rgb(116.4,183,97.8);color:#fff}.bg-gradient-teal-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(28.8, 195.8, 153);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) rgb(28.8,195.8,153);color:#fff}.bg-gradient-teal-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(40, 181.4, 178.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) rgb(40,181.4,178.2);color:#fff}.bg-gradient-cyan-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(48.8, 116, 163.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #2c3e50 var(--bg-gradient-end, 180%)) rgb(48.8,116,163.4);color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(72, 97.6, 228.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(72,97.6,228.2);color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(75.6, 117.6, 208.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) rgb(75.6,117.6,208.6);color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(124, 116, 187.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(124,116,187.4);color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(123.6, 121.6, 155.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) rgb(123.6,121.6,155.4);color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(132.4, 141.6, 139.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) rgb(132.4,141.6,139.4);color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(128.4, 153.6, 138.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) rgb(128.4,153.6,138.6);color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(40.8, 166.4, 193.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #18bc9c var(--bg-gradient-end, 180%)) rgb(40.8,166.4,193.8);color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(44, 171.6, 191.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(44,171.6,191.8);color:#fff}:root{--bslib-spacer: 1rem;--bslib-mb-spacer: var(--bslib-spacer, 1rem)}.bslib-mb-spacing{margin-bottom:var(--bslib-mb-spacer)}.bslib-gap-spacing{gap:var(--bslib-mb-spacer)}.bslib-gap-spacing>.bslib-mb-spacing,.bslib-gap-spacing>.form-group,.bslib-gap-spacing>p,.bslib-gap-spacing>pre{margin-bottom:0}.html-fill-container>.html-fill-item.bslib-mb-spacing{margin-bottom:0}.tab-content>.tab-pane.html-fill-container{display:none}.tab-content>.active.html-fill-container{display:flex}.tab-content.html-fill-container{padding:0}.accordion .accordion-header{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2;color:var(--bs-heading-color);margin-bottom:0}@media(min-width: 1200px){.accordion .accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion .accordion-button:not(.collapsed):focus{box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px 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rgb(26.4, 37.2, 48);--quarto-scss-export-blue-800: rgb(17.6, 24.8, 32);--quarto-scss-export-blue-900: rgb(8.8, 12.4, 16);--quarto-scss-export-indigo-100: rgb(224.4, 207.2, 252.4);--quarto-scss-export-indigo-200: rgb(193.8, 159.4, 249.8);--quarto-scss-export-indigo-300: rgb(163.2, 111.6, 247.2);--quarto-scss-export-indigo-400: rgb(132.6, 63.8, 244.6);--quarto-scss-export-indigo-500: #6610f2;--quarto-scss-export-indigo-600: rgb(81.6, 12.8, 193.6);--quarto-scss-export-indigo-700: rgb(61.2, 9.6, 145.2);--quarto-scss-export-indigo-800: rgb(40.8, 6.4, 96.8);--quarto-scss-export-indigo-900: rgb(20.4, 3.2, 48.4);--quarto-scss-export-purple-100: rgb(226.2, 217.2, 242.6);--quarto-scss-export-purple-200: rgb(197.4, 179.4, 230.2);--quarto-scss-export-purple-300: rgb(168.6, 141.6, 217.8);--quarto-scss-export-purple-400: rgb(139.8, 103.8, 205.4);--quarto-scss-export-purple-500: #6f42c1;--quarto-scss-export-purple-600: rgb(88.8, 52.8, 154.4);--quarto-scss-export-purple-700: rgb(66.6, 39.6, 115.8);--quarto-scss-export-purple-800: rgb(44.4, 26.4, 77.2);--quarto-scss-export-purple-900: rgb(22.2, 13.2, 38.6);--quarto-scss-export-pink-100: rgb(250.4, 216.4, 232);--quarto-scss-export-pink-200: rgb(245.8, 177.8, 209);--quarto-scss-export-pink-300: rgb(241.2, 139.2, 186);--quarto-scss-export-pink-400: rgb(236.6, 100.6, 163);--quarto-scss-export-pink-500: #e83e8c;--quarto-scss-export-pink-600: rgb(185.6, 49.6, 112);--quarto-scss-export-pink-700: rgb(139.2, 37.2, 84);--quarto-scss-export-pink-800: rgb(92.8, 24.8, 56);--quarto-scss-export-pink-900: rgb(46.4, 12.4, 28);--quarto-scss-export-red-100: rgb(250.2, 219.2, 216);--quarto-scss-export-red-200: rgb(245.4, 183.4, 177);--quarto-scss-export-red-300: rgb(240.6, 147.6, 138);--quarto-scss-export-red-400: rgb(235.8, 111.8, 99);--quarto-scss-export-red-500: #e74c3c;--quarto-scss-export-red-600: rgb(184.8, 60.8, 48);--quarto-scss-export-red-700: rgb(138.6, 45.6, 36);--quarto-scss-export-red-800: rgb(92.4, 30.4, 24);--quarto-scss-export-red-900: rgb(46.2, 15.2, 12);--quarto-scss-export-orange-100: rgb(254.6, 229.2, 208);--quarto-scss-export-orange-200: rgb(254.2, 203.4, 161);--quarto-scss-export-orange-300: rgb(253.8, 177.6, 114);--quarto-scss-export-orange-400: rgb(253.4, 151.8, 67);--quarto-scss-export-orange-500: #fd7e14;--quarto-scss-export-orange-600: rgb(202.4, 100.8, 16);--quarto-scss-export-orange-700: rgb(151.8, 75.6, 12);--quarto-scss-export-orange-800: rgb(101.2, 50.4, 8);--quarto-scss-export-orange-900: rgb(50.6, 25.2, 4);--quarto-scss-export-yellow-100: rgb(252.6, 235.2, 207.6);--quarto-scss-export-yellow-200: rgb(250.2, 215.4, 160.2);--quarto-scss-export-yellow-300: rgb(247.8, 195.6, 112.8);--quarto-scss-export-yellow-400: rgb(245.4, 175.8, 65.4);--quarto-scss-export-yellow-500: #f39c12;--quarto-scss-export-yellow-600: rgb(194.4, 124.8, 14.4);--quarto-scss-export-yellow-700: rgb(145.8, 93.6, 10.8);--quarto-scss-export-yellow-800: rgb(97.2, 62.4, 7.2);--quarto-scss-export-yellow-900: rgb(48.6, 31.2, 3.6);--quarto-scss-export-green-100: rgb(208.8, 241.6, 235.2);--quarto-scss-export-green-200: rgb(162.6, 228.2, 215.4);--quarto-scss-export-green-300: rgb(116.4, 214.8, 195.6);--quarto-scss-export-green-400: rgb(70.2, 201.4, 175.8);--quarto-scss-export-green-500: #18bc9c;--quarto-scss-export-green-600: rgb(19.2, 150.4, 124.8);--quarto-scss-export-green-700: rgb(14.4, 112.8, 93.6);--quarto-scss-export-green-800: rgb(9.6, 75.2, 62.4);--quarto-scss-export-green-900: rgb(4.8, 37.6, 31.2);--quarto-scss-export-teal-100: rgb(210.4, 244.2, 234.2);--quarto-scss-export-teal-200: rgb(165.8, 233.4, 213.4);--quarto-scss-export-teal-300: rgb(121.2, 222.6, 192.6);--quarto-scss-export-teal-400: rgb(76.6, 211.8, 171.8);--quarto-scss-export-teal-500: #20c997;--quarto-scss-export-teal-600: rgb(25.6, 160.8, 120.8);--quarto-scss-export-teal-700: rgb(19.2, 120.6, 90.6);--quarto-scss-export-teal-800: rgb(12.8, 80.4, 60.4);--quarto-scss-export-teal-900: rgb(6.4, 40.2, 30.2);--quarto-scss-export-cyan-100: rgb(214.4, 234.4, 247.8);--quarto-scss-export-cyan-200: rgb(173.8, 213.8, 240.6);--quarto-scss-export-cyan-300: rgb(133.2, 193.2, 233.4);--quarto-scss-export-cyan-400: rgb(92.6, 172.6, 226.2);--quarto-scss-export-cyan-500: #3498db;--quarto-scss-export-cyan-600: rgb(41.6, 121.6, 175.2);--quarto-scss-export-cyan-700: rgb(31.2, 91.2, 131.4);--quarto-scss-export-cyan-800: rgb(20.8, 60.8, 87.6);--quarto-scss-export-cyan-900: rgb(10.4, 30.4, 43.8);--quarto-scss-export-default: #6c757d;--quarto-scss-export-primary-text-emphasis: rgb(17.6, 24.8, 32);--quarto-scss-export-secondary-text-emphasis: rgb(43.2, 46.8, 50);--quarto-scss-export-success-text-emphasis: rgb(9.6, 75.2, 62.4);--quarto-scss-export-info-text-emphasis: rgb(20.8, 60.8, 87.6);--quarto-scss-export-warning-text-emphasis: rgb(97.2, 62.4, 7.2);--quarto-scss-export-danger-text-emphasis: rgb(92.4, 30.4, 24);--quarto-scss-export-light-text-emphasis: #7b8a8b;--quarto-scss-export-dark-text-emphasis: #7b8a8b;--quarto-scss-export-primary-bg-subtle: rgb(212.8, 216.4, 220);--quarto-scss-export-secondary-bg-subtle: rgb(225.6, 227.4, 229);--quarto-scss-export-success-bg-subtle: rgb(208.8, 241.6, 235.2);--quarto-scss-export-info-bg-subtle: rgb(214.4, 234.4, 247.8);--quarto-scss-export-warning-bg-subtle: rgb(252.6, 235.2, 207.6);--quarto-scss-export-danger-bg-subtle: rgb(250.2, 219.2, 216);--quarto-scss-export-light-bg-subtle: rgb(251.5, 252, 252.5);--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: rgb(170.6, 177.8, 185);--quarto-scss-export-secondary-border-subtle: rgb(196.2, 199.8, 203);--quarto-scss-export-success-border-subtle: rgb(162.6, 228.2, 215.4);--quarto-scss-export-info-border-subtle: rgb(173.8, 213.8, 240.6);--quarto-scss-export-warning-border-subtle: rgb(250.2, 215.4, 160.2);--quarto-scss-export-danger-border-subtle: rgb(245.4, 183.4, 177);--quarto-scss-export-light-border-subtle: #ecf0f1;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-color: #212529;--quarto-scss-export-body-bg: #fff;--quarto-scss-export-body-secondary-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-body-secondary-bg: #ecf0f1;--quarto-scss-export-body-tertiary-color: rgba(33, 37, 41, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-color: rgb(19.2, 150.4, 124.8);--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #2c3e50;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(44, 62, 80, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #ecf0f1;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: rgb(252.6, 235.2, 207.6);--quarto-scss-export-table-color: #212529;--quarto-scss-export-table-bg: #fff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #212529;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #212529;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #212529;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(44, 62, 80, 0.25);--quarto-scss-export-btn-color: #212529;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #18bc9c;--quarto-scss-export-btn-link-hover-color: rgb(19.2, 150.4, 124.8);--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #ecf0f1;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #212529;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: rgb(149.5, 158.5, 167.5);--quarto-scss-export-input-focus-color: #212529;--quarto-scss-export-input-placeholder-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-input-plaintext-color: #212529;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: rgb(149.5, 158.5, 167.5);--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2c3e50;--quarto-scss-export-form-check-input-checked-border-color: #2c3e50;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2c3e50;--quarto-scss-export-form-check-input-indeterminate-border-color: #2c3e50;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: rgb(149.5, 158.5, 167.5);--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #212529;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #212529;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #ecf0f1;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: rgb(149.5, 158.5, 167.5);--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2c3e50;--quarto-scss-export-form-range-thumb-active-bg: rgb(191.7, 197.1, 202.5);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(33, 37, 41, 0.75);--quarto-scss-export-form-file-button-color: #212529;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #ecf0f1;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #18bc9c;--quarto-scss-export-form-feedback-invalid-color: #e74c3c;--quarto-scss-export-form-feedback-icon-valid-color: #18bc9c;--quarto-scss-export-form-feedback-icon-invalid-color: #e74c3c;--quarto-scss-export-form-valid-color: #18bc9c;--quarto-scss-export-form-valid-border-color: #18bc9c;--quarto-scss-export-form-invalid-color: #e74c3c;--quarto-scss-export-form-invalid-border-color: #e74c3c;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #18bc9c;--quarto-scss-export-nav-link-hover-color: rgb(19.2, 150.4, 124.8);--quarto-scss-export-nav-tabs-link-hover-border-color: #ecf0f1 #ecf0f1 #ecf0f1;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2c3e50;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #fff;--quarto-scss-export-navbar-dark-contrast: #fff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #212529;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-active-bg: #2c3e50;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(33, 37, 41, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2c3e50;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-focus-color: rgb(19.2, 150.4, 124.8);--quarto-scss-export-pagination-focus-bg: #ecf0f1;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #212529;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #212529;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: rgb(212.8, 216.4, 220);--quarto-scss-export-accordion-button-active-color: rgb(17.6, 24.8, 32);--quarto-scss-export-accordion-button-focus-border-color: rgb(149.5, 158.5, 167.5);--quarto-scss-export-accordion-icon-color: #212529;--quarto-scss-export-accordion-icon-active-color: rgb(17.6, 24.8, 32);--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #ecf0f1;--quarto-scss-export-popover-body-color: #212529;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: 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#000;--bs-btn-focus-shadow-rgb: 38, 198, 157;text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}.btn-link:focus-visible{color:var(--bs-btn-color)}.btn-link:hover{color:var(--bs-btn-hover-color)}.btn-lg,.btn-group-lg>.btn{--bs-btn-padding-y: 0.5rem;--bs-btn-padding-x: 1rem;--bs-btn-font-size:1.25rem;--bs-btn-border-radius: 0.5rem}.btn-sm,.btn-group-sm>.btn{--bs-btn-padding-y: 0.25rem;--bs-btn-padding-x: 0.5rem;--bs-btn-font-size:0.875rem;--bs-btn-border-radius: 0.2em}.fade{transition:opacity .15s linear}@media(prefers-reduced-motion: reduce){.fade{transition:none}}.fade:not(.show){opacity:0}.collapse:not(.show){display:none}.collapsing{height:0;overflow:hidden;transition:height .2s ease}@media(prefers-reduced-motion: reduce){.collapsing{transition:none}}.collapsing.collapse-horizontal{width:0;height:auto;transition:width .35s ease}@media(prefers-reduced-motion: 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #dee2e6;--bs-navbar-hover-color: rgba(255, 255, 255, 0.8);--bs-navbar-disabled-color: rgba(222, 226, 230, 0.75);--bs-navbar-active-color: #fff;--bs-navbar-brand-color: #dee2e6;--bs-navbar-brand-hover-color: #fff;--bs-navbar-toggler-border-color: rgba(222, 226, 230, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: rgb(45.05, 45.05, 45.05);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #fff;--bs-accordion-bg: #222;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #fff;--bs-accordion-btn-bg: #222;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%2822, 36, 50.8%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: rgb(155, 172.5, 191);--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: rgb(22, 36, 50.8);--bs-accordion-active-bg: rgb(215, 222, 229.4)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28135, 156, 178.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28135, 156, 178.2%29'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: rgb(67.15, 67.15, 67.15);--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #00bc8c;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: rgb(0, 239, 177.9787234043);--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: rgb(0, 150.4, 112);--bs-pagination-focus-bg: #ebebeb;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: rgb(0, 239, 177.9787234043);--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #fff;--bs-pagination-disabled-bg: rgb(0, 111.5, 83.0319148936);--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: rgb(67.15, 67.15, 67.15);--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #375a7f;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #fff;--bs-list-group-bg: rgb(45.05, 45.05, 45.05);--bs-list-group-border-color: rgb(67.15, 67.15, 67.15);--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #fff;--bs-list-group-action-hover-bg: rgb(67.15, 67.15, 67.15);--bs-list-group-action-active-color: #fff;--bs-list-group-action-active-bg: #222;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: rgb(45.05, 45.05, 45.05);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #375a7f;--bs-list-group-active-border-color: #375a7f;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(55, 90, 127, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgb(67.15, 67.15, 67.15);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(255, 255, 255, 0.75);--bs-toast-header-bg: rgb(45.05, 45.05, 45.05);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: rgb(45.05, 45.05, 45.05);--bs-modal-border-color: rgb(67.15, 67.15, 67.15);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: rgb(67.15, 67.15, 67.15);--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: rgb(67.15, 67.15, 67.15);--bs-modal-footer-border-width: 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36);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) rgb(244.2,106,36);color:#fff}.bg-gradient-orange-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(249, 138, 19.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) rgb(249,138,19.2);color:#fff}.bg-gradient-orange-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(151.8, 150.8, 68);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #00bc8c var(--bg-gradient-end, 180%)) rgb(151.8,150.8,68);color:#fff}.bg-gradient-orange-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(164.6, 156, 72.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(164.6,156,72.4);color:#fff}.bg-gradient-orange-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(172.6, 136.4, 99.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #fd7e14 var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) rgb(172.6,136.4,99.6);color:#fff}.bg-gradient-yellow-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(167.8, 129.6, 61.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #375a7f var(--bg-gradient-end, 180%)) rgb(167.8,129.6,61.6);color:#fff}.bg-gradient-yellow-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(186.6, 100, 107.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(186.6,100,107.6);color:#fff}.bg-gradient-yellow-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(190.2, 120, 88);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) 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var(--bg-gradient-end, 180%)) rgb(145.8,168.8,66.8);color:#fff}.bg-gradient-yellow-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(158.6, 174, 71.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(158.6,174,71.2);color:#fff}.bg-gradient-yellow-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(166.6, 154.4, 98.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #f39c12 var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) rgb(166.6,154.4,98.4);color:#fff}.bg-gradient-green-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(22, 148.8, 134.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #00bc8c var(--bg-gradient-start, 36%), #375a7f var(--bg-gradient-end, 180%)) rgb(22,148.8,134.8);color:#fff}.bg-gradient-green-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(40.8, 119.2, 180.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #00bc8c var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(40.8,119.2,180.8);color:#fff}.bg-gradient-green-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(44.4, 139.2, 161.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #00bc8c var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) rgb(44.4,139.2,161.2);color:#fff}.bg-gradient-green-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(92.8, 137.6, 140);background:linear-gradient(var(--bg-gradient-deg, 140deg), #00bc8c var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(92.8,137.6,140);color:#fff}.bg-gradient-green-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(92.4, 143.2, 108);background:linear-gradient(var(--bg-gradient-deg, 140deg), #00bc8c var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) rgb(92.4,143.2,108);color:#fff}.bg-gradient-green-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(101.2, 163.2, 92);background:linear-gradient(var(--bg-gradient-deg, 140deg), #00bc8c var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) rgb(101.2,163.2,92);color:#fff}.bg-gradient-green-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(97.2, 175.2, 91.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #00bc8c var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) rgb(97.2,175.2,91.2);color:#fff}.bg-gradient-green-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(12.8, 193.2, 144.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #00bc8c var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(12.8,193.2,144.4);color:#fff}.bg-gradient-green-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(20.8, 173.6, 171.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #00bc8c var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) rgb(20.8,173.6,171.6);color:#fff}.bg-gradient-teal-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(41.2, 156.6, 141.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #375a7f var(--bg-gradient-end, 180%)) rgb(41.2,156.6,141.4);color:#fff}.bg-gradient-teal-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(60, 127, 187.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(60,127,187.4);color:#fff}.bg-gradient-teal-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(63.6, 147, 167.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) rgb(63.6,147,167.8);color:#fff}.bg-gradient-teal-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(112, 145.4, 146.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(112,145.4,146.6);color:#fff}.bg-gradient-teal-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(111.6, 151, 114.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) rgb(111.6,151,114.6);color:#fff}.bg-gradient-teal-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(120.4, 171, 98.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) rgb(120.4,171,98.6);color:#fff}.bg-gradient-teal-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(116.4, 183, 97.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) rgb(116.4,183,97.8);color:#fff}.bg-gradient-teal-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(19.2, 195.8, 146.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #00bc8c var(--bg-gradient-end, 180%)) rgb(19.2,195.8,146.6);color:#fff}.bg-gradient-teal-cyan{--bslib-color-fg: #fff;--bslib-color-bg: rgb(40, 181.4, 178.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #20c997 var(--bg-gradient-start, 36%), #3498db var(--bg-gradient-end, 180%)) rgb(40,181.4,178.2);color:#fff}.bg-gradient-cyan-blue{--bslib-color-fg: #fff;--bslib-color-bg: rgb(53.2, 127.2, 182.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #375a7f var(--bg-gradient-end, 180%)) rgb(53.2,127.2,182.2);color:#fff}.bg-gradient-cyan-indigo{--bslib-color-fg: #fff;--bslib-color-bg: rgb(72, 97.6, 228.2);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #6610f2 var(--bg-gradient-end, 180%)) rgb(72,97.6,228.2);color:#fff}.bg-gradient-cyan-purple{--bslib-color-fg: #fff;--bslib-color-bg: rgb(75.6, 117.6, 208.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) rgb(75.6,117.6,208.6);color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: rgb(124, 116, 187.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) rgb(124,116,187.4);color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: rgb(123.6, 121.6, 155.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) rgb(123.6,121.6,155.4);color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: rgb(132.4, 141.6, 139.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) rgb(132.4,141.6,139.4);color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: rgb(128.4, 153.6, 138.6);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) rgb(128.4,153.6,138.6);color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: rgb(31.2, 166.4, 187.4);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #00bc8c var(--bg-gradient-end, 180%)) rgb(31.2,166.4,187.4);color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: rgb(44, 171.6, 191.8);background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) rgb(44,171.6,191.8);color:#fff}:root{--bslib-spacer: 1rem;--bslib-mb-spacer: var(--bslib-spacer, 1rem)}.bslib-mb-spacing{margin-bottom:var(--bslib-mb-spacer)}.bslib-gap-spacing{gap:var(--bslib-mb-spacer)}.bslib-gap-spacing>.bslib-mb-spacing,.bslib-gap-spacing>.form-group,.bslib-gap-spacing>p,.bslib-gap-spacing>pre{margin-bottom:0}.html-fill-container>.html-fill-item.bslib-mb-spacing{margin-bottom:0}.tab-content>.tab-pane.html-fill-container{display:none}.tab-content>.active.html-fill-container{display:flex}.tab-content.html-fill-container{padding:0}.accordion .accordion-header{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2;color:var(--bs-heading-color);margin-bottom:0}@media(min-width: 1200px){.accordion .accordion-header{font-size:1.65rem}}.accordion .accordion-icon:not(:empty){margin-right:.75rem;display:flex}.accordion .accordion-button:not(.collapsed){box-shadow:none}.accordion .accordion-button:not(.collapsed):focus{box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen 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193.6);--quarto-scss-export-indigo-700: rgb(61.2, 9.6, 145.2);--quarto-scss-export-indigo-800: rgb(40.8, 6.4, 96.8);--quarto-scss-export-indigo-900: rgb(20.4, 3.2, 48.4);--quarto-scss-export-purple-100: rgb(226.2, 217.2, 242.6);--quarto-scss-export-purple-200: rgb(197.4, 179.4, 230.2);--quarto-scss-export-purple-300: rgb(168.6, 141.6, 217.8);--quarto-scss-export-purple-400: rgb(139.8, 103.8, 205.4);--quarto-scss-export-purple-500: #6f42c1;--quarto-scss-export-purple-600: rgb(88.8, 52.8, 154.4);--quarto-scss-export-purple-700: rgb(66.6, 39.6, 115.8);--quarto-scss-export-purple-800: rgb(44.4, 26.4, 77.2);--quarto-scss-export-purple-900: rgb(22.2, 13.2, 38.6);--quarto-scss-export-pink-100: rgb(250.4, 216.4, 232);--quarto-scss-export-pink-200: rgb(245.8, 177.8, 209);--quarto-scss-export-pink-300: rgb(241.2, 139.2, 186);--quarto-scss-export-pink-400: rgb(236.6, 100.6, 163);--quarto-scss-export-pink-500: #e83e8c;--quarto-scss-export-pink-600: rgb(185.6, 49.6, 112);--quarto-scss-export-pink-700: rgb(139.2, 37.2, 84);--quarto-scss-export-pink-800: rgb(92.8, 24.8, 56);--quarto-scss-export-pink-900: rgb(46.4, 12.4, 28);--quarto-scss-export-red-100: rgb(250.2, 219.2, 216);--quarto-scss-export-red-200: rgb(245.4, 183.4, 177);--quarto-scss-export-red-300: rgb(240.6, 147.6, 138);--quarto-scss-export-red-400: rgb(235.8, 111.8, 99);--quarto-scss-export-red-500: #e74c3c;--quarto-scss-export-red-600: rgb(184.8, 60.8, 48);--quarto-scss-export-red-700: rgb(138.6, 45.6, 36);--quarto-scss-export-red-800: rgb(92.4, 30.4, 24);--quarto-scss-export-red-900: rgb(46.2, 15.2, 12);--quarto-scss-export-orange-100: rgb(254.6, 229.2, 208);--quarto-scss-export-orange-200: rgb(254.2, 203.4, 161);--quarto-scss-export-orange-300: rgb(253.8, 177.6, 114);--quarto-scss-export-orange-400: rgb(253.4, 151.8, 67);--quarto-scss-export-orange-500: #fd7e14;--quarto-scss-export-orange-600: rgb(202.4, 100.8, 16);--quarto-scss-export-orange-700: rgb(151.8, 75.6, 12);--quarto-scss-export-orange-800: rgb(101.2, 50.4, 8);--quarto-scss-export-orange-900: rgb(50.6, 25.2, 4);--quarto-scss-export-yellow-100: rgb(252.6, 235.2, 207.6);--quarto-scss-export-yellow-200: rgb(250.2, 215.4, 160.2);--quarto-scss-export-yellow-300: rgb(247.8, 195.6, 112.8);--quarto-scss-export-yellow-400: rgb(245.4, 175.8, 65.4);--quarto-scss-export-yellow-500: #f39c12;--quarto-scss-export-yellow-600: rgb(194.4, 124.8, 14.4);--quarto-scss-export-yellow-700: rgb(145.8, 93.6, 10.8);--quarto-scss-export-yellow-800: rgb(97.2, 62.4, 7.2);--quarto-scss-export-yellow-900: rgb(48.6, 31.2, 3.6);--quarto-scss-export-green-100: rgb(204, 241.6, 232);--quarto-scss-export-green-200: rgb(153, 228.2, 209);--quarto-scss-export-green-300: rgb(102, 214.8, 186);--quarto-scss-export-green-400: rgb(51, 201.4, 163);--quarto-scss-export-green-500: #00bc8c;--quarto-scss-export-green-600: rgb(0, 150.4, 112);--quarto-scss-export-green-700: rgb(0, 112.8, 84);--quarto-scss-export-green-800: rgb(0, 75.2, 56);--quarto-scss-export-green-900: rgb(0, 37.6, 28);--quarto-scss-export-teal-100: rgb(210.4, 244.2, 234.2);--quarto-scss-export-teal-200: rgb(165.8, 233.4, 213.4);--quarto-scss-export-teal-300: rgb(121.2, 222.6, 192.6);--quarto-scss-export-teal-400: rgb(76.6, 211.8, 171.8);--quarto-scss-export-teal-500: #20c997;--quarto-scss-export-teal-600: rgb(25.6, 160.8, 120.8);--quarto-scss-export-teal-700: rgb(19.2, 120.6, 90.6);--quarto-scss-export-teal-800: rgb(12.8, 80.4, 60.4);--quarto-scss-export-teal-900: rgb(6.4, 40.2, 30.2);--quarto-scss-export-cyan-100: rgb(214.4, 234.4, 247.8);--quarto-scss-export-cyan-200: rgb(173.8, 213.8, 240.6);--quarto-scss-export-cyan-300: rgb(133.2, 193.2, 233.4);--quarto-scss-export-cyan-400: rgb(92.6, 172.6, 226.2);--quarto-scss-export-cyan-500: #3498db;--quarto-scss-export-cyan-600: rgb(41.6, 121.6, 175.2);--quarto-scss-export-cyan-700: rgb(31.2, 91.2, 131.4);--quarto-scss-export-cyan-800: rgb(20.8, 60.8, 87.6);--quarto-scss-export-cyan-900: rgb(10.4, 30.4, 43.8);--quarto-scss-export-default: rgb(67.15, 67.15, 67.15);--quarto-scss-export-primary-text-emphasis: rgb(22, 36, 50.8);--quarto-scss-export-secondary-text-emphasis: rgb(26.86, 26.86, 26.86);--quarto-scss-export-success-text-emphasis: rgb(0, 75.2, 56);--quarto-scss-export-info-text-emphasis: rgb(20.8, 60.8, 87.6);--quarto-scss-export-warning-text-emphasis: rgb(97.2, 62.4, 7.2);--quarto-scss-export-danger-text-emphasis: rgb(92.4, 30.4, 24);--quarto-scss-export-light-text-emphasis: #444;--quarto-scss-export-dark-text-emphasis: #444;--quarto-scss-export-primary-bg-subtle: rgb(215, 222, 229.4);--quarto-scss-export-secondary-bg-subtle: rgb(217.43, 217.43, 217.43);--quarto-scss-export-success-bg-subtle: rgb(204, 241.6, 232);--quarto-scss-export-info-bg-subtle: rgb(214.4, 234.4, 247.8);--quarto-scss-export-warning-bg-subtle: rgb(252.6, 235.2, 207.6);--quarto-scss-export-danger-bg-subtle: rgb(250.2, 219.2, 216);--quarto-scss-export-light-bg-subtle: rgb(251.5, 252, 252.5);--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: rgb(175, 189, 203.8);--quarto-scss-export-secondary-border-subtle: rgb(179.86, 179.86, 179.86);--quarto-scss-export-success-border-subtle: rgb(153, 228.2, 209);--quarto-scss-export-info-border-subtle: rgb(173.8, 213.8, 240.6);--quarto-scss-export-warning-border-subtle: rgb(250.2, 215.4, 160.2);--quarto-scss-export-danger-border-subtle: rgb(245.4, 183.4, 177);--quarto-scss-export-light-border-subtle: #ebebeb;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-secondary-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-body-secondary-bg: #ebebeb;--quarto-scss-export-body-tertiary-color: rgba(255, 255, 255, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-color: rgb(0, 150.4, 112);--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #375a7f;--quarto-scss-export-component-active-color: #fff;--quarto-scss-export-focus-ring-color: rgba(55, 90, 127, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #ebebeb;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: rgb(252.6, 235.2, 207.6);--quarto-scss-export-table-color: #fff;--quarto-scss-export-table-bg: #222;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #fff;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #fff;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #fff;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(55, 90, 127, 0.25);--quarto-scss-export-btn-color: #fff;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #00bc8c;--quarto-scss-export-btn-link-hover-color: rgb(0, 150.4, 112);--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #ebebeb;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: rgb(155, 172.5, 191);--quarto-scss-export-input-plaintext-color: #fff;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-focus-border: rgb(155, 172.5, 191);--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #375a7f;--quarto-scss-export-form-check-input-checked-border-color: #375a7f;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #375a7f;--quarto-scss-export-form-check-input-indeterminate-border-color: #375a7f;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: rgb(155, 172.5, 191);--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-border-color: #adb5bd;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-bg: #ebebeb;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #adb5bd;--quarto-scss-export-form-select-focus-border-color: rgb(155, 172.5, 191);--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #375a7f;--quarto-scss-export-form-range-thumb-active-bg: rgb(195, 205.5, 216.6);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(255, 255, 255, 0.75);--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #00bc8c;--quarto-scss-export-form-feedback-invalid-color: #e74c3c;--quarto-scss-export-form-feedback-icon-valid-color: #00bc8c;--quarto-scss-export-form-feedback-icon-invalid-color: #e74c3c;--quarto-scss-export-form-valid-color: #00bc8c;--quarto-scss-export-form-valid-border-color: #00bc8c;--quarto-scss-export-form-invalid-color: #e74c3c;--quarto-scss-export-form-invalid-border-color: #e74c3c;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #00bc8c;--quarto-scss-export-nav-link-hover-color: rgb(0, 150.4, 112);--quarto-scss-export-nav-tabs-link-active-bg: #222;--quarto-scss-export-nav-pills-link-active-bg: #375a7f;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #fff;--quarto-scss-export-navbar-dark-contrast: #fff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #fff;--quarto-scss-export-dropdown-link-active-bg: #375a7f;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(255, 255, 255, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #375a7f;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-focus-color: rgb(0, 150.4, 112);--quarto-scss-export-pagination-focus-bg: #ebebeb;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-accordion-color: #fff;--quarto-scss-export-accordion-bg: #222;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #fff;--quarto-scss-export-accordion-button-bg: #222;--quarto-scss-export-accordion-button-active-bg: rgb(215, 222, 229.4);--quarto-scss-export-accordion-button-active-color: rgb(22, 36, 50.8);--quarto-scss-export-accordion-button-focus-border-color: rgb(155, 172.5, 191);--quarto-scss-export-accordion-icon-color: #fff;--quarto-scss-export-accordion-icon-active-color: rgb(22, 36, 50.8);--quarto-scss-export-tooltip-color: #222;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-body-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #375a7f;--quarto-scss-export-list-group-active-bg: #375a7f;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #375a7f;--quarto-scss-export-list-group-disabled-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-list-group-action-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-list-group-action-active-color: #fff;--quarto-scss-export-thumbnail-bg: #222;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-offcanvas-bg-color: #222;--quarto-scss-export-offcanvas-color: #fff;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #222;--quarto-scss-export-kbd-bg: #fff;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-bslib-page-sidebar-title-bg: #375a7f;--quarto-scss-export-bslib-page-sidebar-title-color: #fff;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: rgb(253.5635, 253.5635, 253.5635);--quarto-scss-export-sidebar-hover-color: rgba(252.45, 254.33, 253.85, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(253.5635, 253.5635, 253.5635, 0.75);--quarto-scss-export-valuebox-bg-primary: #375a7f;--quarto-scss-export-valuebox-bg-success: #00bc8c;--quarto-scss-export-valuebox-bg-info: #3498db;--quarto-scss-export-valuebox-bg-warning: #f39c12;--quarto-scss-export-valuebox-bg-danger: #e74c3c;--quarto-scss-export-mermaid-bg-color: #222;--quarto-scss-export-mermaid-node-fg-color: #fff;--quarto-scss-export-mermaid-fg-color: #fff;--quarto-scss-export-mermaid-fg-color--lighter: white;--quarto-scss-export-mermaid-fg-color--lightest: white;--quarto-scss-export-mermaid-label-bg-color: #222;--quarto-scss-export-mermaid-label-fg-color: #375a7f;--quarto-scss-export-mermaid-node-bg-color: rgba(55, 90, 127, 0.1);--quarto-scss-export-callout-color-note: #375a7f;--quarto-scss-export-callout-color-tip: #00bc8c;--quarto-scss-export-callout-color-important: #e74c3c;--quarto-scss-export-callout-color-caution: #fd7e14;--quarto-scss-export-callout-color-warning: #f39c12} \ No newline at end of file diff --git a/rfaR-book/_book/site_libs/bootstrap/bootstrap-icons.css b/rfaR-book/_book/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..82b40f5 --- /dev/null +++ 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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return 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b/rfaR-book/_book/site_libs/quarto-html/quarto-syntax-highlighting-7f8f88aac4f3542376d5c11b86a4c14d.css new file mode 100644 index 0000000..b60b56f --- /dev/null +++ b/rfaR-book/_book/site_libs/quarto-html/quarto-syntax-highlighting-7f8f88aac4f3542376d5c11b86a4c14d.css @@ -0,0 +1,236 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +/* syntax highlight based on Pandoc's rules */ +pre > code.sourceCode > span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +/* Normal */ +code span { + color: #003B4F; +} + +/* Alert */ +code span.al { + color: #AD0000; + font-style: inherit; +} + +/* Annotation */ +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +/* Attribute */ +code span.at { + color: #657422; + font-style: inherit; +} + +/* BaseN */ +code span.bn { + color: #AD0000; + font-style: inherit; +} + +/* BuiltIn */ +code span.bu { + font-style: inherit; +} + +/* ControlFlow */ +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +/* Char */ +code span.ch { + color: #20794D; + font-style: inherit; +} + +/* Constant */ +code span.cn { + color: #8f5902; + font-style: inherit; +} + +/* Comment */ +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +/* CommentVar */ +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +/* Documentation */ +code span.do { + color: #5E5E5E; + font-style: italic; +} + +/* DataType */ +code span.dt { + color: #AD0000; + font-style: inherit; +} + +/* DecVal */ +code span.dv { + color: #AD0000; + font-style: inherit; +} + +/* Error */ +code span.er { + color: #AD0000; + font-style: inherit; +} + +/* Extension */ +code span.ex { + font-style: inherit; +} + +/* Float */ +code span.fl { + color: #AD0000; + font-style: inherit; +} + +/* Function */ +code span.fu { + color: #4758AB; + font-style: inherit; +} + +/* Import */ +code span.im { + color: #00769E; + font-style: inherit; +} + +/* Information */ +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +/* Keyword */ +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +/* Operator */ +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +/* Other */ +code span.ot { + color: #003B4F; + font-style: inherit; +} + +/* Preprocessor */ +code span.pp { + color: #AD0000; + font-style: inherit; +} + +/* SpecialChar */ +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +/* SpecialString */ +code span.ss { + color: #20794D; + font-style: inherit; +} + +/* String */ +code span.st { + color: #20794D; + font-style: inherit; +} + +/* Variable */ +code span.va { + color: #111111; + font-style: inherit; +} + +/* VerbatimString */ +code span.vs { + color: #20794D; + font-style: inherit; +} + +/* Warning */ +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +.prevent-inlining { + content: " code.sourceCode > span { + color: #f8f8f2; +} + +code.sourceCode > span { + color: #f8f8f2; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #f8f8f2; +} + +/* Normal */ +code span { + color: #f8f8f2; +} + +/* Alert */ +code span.al { + color: #f07178; +} + +/* Annotation */ +code span.an { + color: #d4d0ab; +} + +/* Attribute */ +code span.at { + color: #00e0e0; +} + +/* BaseN */ +code span.bn { + color: #d4d0ab; +} + +/* BuiltIn */ +code span.bu { + color: #abe338; +} + +/* ControlFlow */ +code span.cf { + font-weight: bold; + color: #ffa07a; +} + +/* Char */ +code span.ch { + color: #abe338; +} + +/* Constant */ +code span.cn { + color: #ffd700; +} + +/* Comment */ +code span.co { + font-style: italic; + color: #f8f8f2; +} + +/* CommentVar */ +code span.cv { + color: #ffd700; +} + +/* Documentation */ +code span.do { + color: #f8f8f2; +} + +/* DataType */ +code span.dt { + color: #ffa07a; +} + +/* DecVal */ +code span.dv { + color: #d4d0ab; +} + +/* Error */ +code span.er { + color: #f07178; + text-decoration: underline; +} + +/* Extension */ +code span.ex { + font-weight: bold; + color: #00e0e0; +} + +/* Float */ +code span.fl { + color: #d4d0ab; +} + +/* Function */ +code span.fu { + color: #ffa07a; +} + +/* Import */ +code span.im { + color: #abe338; +} + +/* Information */ +code span.in { + color: #d4d0ab; +} + +/* Keyword */ +code span.kw { + font-weight: bold; + color: #ffa07a; +} + +/* Operator */ +code span.op { + color: #ffa07a; +} + +/* Other */ +code span.ot { + color: #00e0e0; +} + +/* Preprocessor */ +code span.pp { + color: #dcc6e0; +} + +/* RegionMarker */ +code span.re { + background-color: #f8f8f2; + color: #00e0e0; +} + +/* SpecialChar */ +code span.sc { + color: #abe338; +} + +/* SpecialString */ +code span.ss { + color: #abe338; +} + +/* String */ +code span.st { + color: #abe338; +} + +/* Variable */ +code span.va { + color: #00e0e0; +} + +/* VerbatimString */ +code span.vs { + color: #abe338; +} + +/* Warning */ +code span.wa { + color: #dcc6e0; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // dispatch for htmlwidgets + // they use slideenter event to trigger resize + function fireSlideEnter() { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // dispatch for shiny + // they use BS shown and hidden events to trigger rendering + function distpatchShinyEvents(previous, current) { + if (window.jQuery) { + if (previous) { + window.jQuery(previous).trigger("hidden"); + } + if (current) { + window.jQuery(current).trigger("shown"); + } + } + } + + // tabby.js listener: Trigger event for htmlwidget and shiny + document.addEventListener( + "tabby", + function (event) { + fireSlideEnter(); + distpatchShinyEvents(event.detail.previousTab, event.detail.tab); + }, + false + ); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + // Categories search with listing only use path without query + const currentPagePath = offsetAbsoluteUrl( + window.location.origin + window.location.pathname + ); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + const encodedItem = encodeURI(item); + if ( + encodedItem === currentPagePath || + encodedItem === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (const child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +tabsets.init(); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/rfaR-book/_book/site_libs/quarto-html/tabsets/tabsets.js b/rfaR-book/_book/site_libs/quarto-html/tabsets/tabsets.js new file mode 100644 index 0000000..51345d0 --- /dev/null +++ b/rfaR-book/_book/site_libs/quarto-html/tabsets/tabsets.js @@ -0,0 +1,95 @@ +// grouped tabsets + +export function init() { + window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } + }); +} diff --git a/rfaR-book/_book/site_libs/quarto-html/tippy.css b/rfaR-book/_book/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/rfaR-book/_book/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 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0000000..38cc430 --- /dev/null +++ b/rfaR-book/_book/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); 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links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/rfaR-book/_book/site_libs/quarto-search/autocomplete.umd.js b/rfaR-book/_book/site_libs/quarto-search/autocomplete.umd.js new file 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Apache Software License 2.0 + * + * http://www.apache.org/licenses/LICENSE-2.0 + */ +var e,t;e=this,t=function(){"use strict";function e(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function t(t){for(var n=1;ne.length)&&(t=e.length);for(var n=0,r=new Array(t);n0&&void 0!==arguments[0]?arguments[0]:1,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3,n=new Map,r=Math.pow(10,t);return{get:function(t){var i=t.match(C).length;if(n.has(i))return n.get(i);var o=1/Math.pow(i,.5*e),c=parseFloat(Math.round(o*r)/r);return n.set(i,c),c},clear:function(){n.clear()}}}var $=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=t.getFn,i=void 0===n?I.getFn:n,o=t.fieldNormWeight,c=void 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a/rfaR-book/_book/site_libs/quarto-search/quarto-search.js b/rfaR-book/_book/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..437e38f --- /dev/null +++ b/rfaR-book/_book/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1457 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +// Capture search params and clean ?q= from URL at module load time, before +// any DOMContentLoaded handlers run. quarto-nav.js resolves all     hrefs +// against window.location during DOMContentLoaded — if ?q= is still present, +// every link on the page gets the query param baked into its href. +const currentUrl = new URL(window.location); +const kQuery = currentUrl.searchParams.get(kQueryArg); +if (kQuery) { + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); +} + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const query = kQuery; + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + highlight(query, mainEl); + + // Activate tabs on pageshow — after tabsets.js restores localStorage state. + // tabsets.js registers its pageshow handler during module execution (before + // DOMContentLoaded). By registering ours during DOMContentLoaded, listener + // ordering guarantees we run after tabsets.js — so search activation wins. + window.addEventListener("pageshow", function (event) { + if (!event.persisted) { + for (const mark of mainEl.querySelectorAll("mark")) { + openAllTabsetsContainingEl(mark); + } + // Only scroll to first match when there's no hash fragment. + // With a hash, the browser already scrolled to the target section. + if (!currentUrl.hash) { + requestAnimationFrame(() => scrollToFirstVisibleMatch(mainEl)); + } + } + }, { once: true }); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + if (item.text && item.href && !item.href.includes('?q=')) { + const [main, hash] = item.href.split('#') + const hashAppend = hash ? '#' + hash : '' + item.href = main + '?q=' + encodeURIComponent(state.query) + hashAppend + } + + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + // Check if cookie consent is enabled from search options + const cookieConsentEnabled = algoliaOptions["cookie-consent-enabled"] || false; + + // Generate random session token only when cookies are disabled + const userToken = cookieConsentEnabled ? undefined : Array.from(Array(20), () => + Math.floor(Math.random() * 36).toString(36) + ).join(""); + + window.aa("init", { + appId, + apiKey, + useCookie: cookieConsentEnabled, + userToken: userToken, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +/** Get all html nodes under the given `root` that don't have children. */ +function getLeafNodes(root) { + let leaves = []; + + function traverse(node) { + if (node.childNodes.length === 0) { + leaves.push(node); + } else { + node.childNodes.forEach(traverse); + } + } + + traverse(root); + return leaves; +} +/** create and return `${txt}` */ +const markEl = txt => { + const el = document.createElement("mark"); + el.appendChild(document.createTextNode(txt)); + return el +} +/** get all ancestors of an element matching the given css selector */ +const matchAncestors = (el, selector) => { + let ancestors = []; + while (el) { + if (el.matches?.(selector)) ancestors.push(el); + el = el.parentNode; + } + return ancestors; +}; + +const isWhitespace = s => s.trim().length === 0 +// ================= +// MATCHING CODE +// ================= +const initMatch = () => ({ + i: 0, + lohisByNode: new Map() +}) +/** + * keeps track of the start (lo) and end (hi) index of the match per node (leaf) + * note: mutates the contents of `matchContext` + */ +const advanceMatch = (leaf, leafi, matchContext) => { + matchContext.i++ + + const curLoHi = matchContext.lohisByNode.get(leaf) + + matchContext.lohisByNode.set(leaf, { lo: curLoHi?.lo ?? leafi, hi: leafi }) +} +/** + * Finds all non-overlapping matches for a search string in the document. + * The search string may be split between multiple consecutive leaf nodes. + * + * Whitespace in the search string must be present in the document to match, but + * there may be addititional whitespace in the document that is ignored. + * + * e.g. searching for `dogs rock` would match `dogs \n rock`, + * and would contribute the match + * `{ i:9, els: new Map([[textNode, {lo:0, hi:8}],[spanNode,{lo:0,hi:5}]]) }` + * + * @returns {Map[]} + */ +function searchMatches(inSearch, el) { + // searchText has all sequences of whitespace replaced by a single space + const searchText = inSearch.toLowerCase().replace(/\s+/g, ' ') + const leafNodes = getLeafNodes(el) + + /** @type {Map[]} */ + const matches = [] + /** @type {{i:number; els:Map}[]} */ + let curMatchContext = initMatch() + + for (const leaf of leafNodes) { + const leafStr = leaf.textContent.toLowerCase() + // for each character in this leaf's text: + for (let leafi = 0; leafi < leafStr.length; leafi++) { + + if (isWhitespace(leafStr[leafi])) { + // if there is at least one whitespace in the document + // we advance over a search text whitespace. + if (isWhitespace(searchText[curMatchContext.i])) advanceMatch(leaf, leafi, curMatchContext) + // all sequences of whitespace are otherwise ignored. + } else { + if (searchText[curMatchContext.i] === leafStr[leafi]) { + advanceMatch(leaf, leafi, curMatchContext) + } else { + curMatchContext = initMatch() + // if current character in the document did not match at i in the search text, + // reset the search and see if that character matches at 0 in the search text. + if (searchText[curMatchContext.i] === leafStr[leafi]) advanceMatch(leaf, leafi, curMatchContext) + } + } + + const isMatchComplete = curMatchContext.i === searchText.length + if (isMatchComplete) { + matches.push(curMatchContext.lohisByNode) + curMatchContext = initMatch() + } + } + } + + return matches +} + +/** + * e.g. `markMatches(myTextNode, [[0,5],[12,15]])` would wrap the + * character sequences in myTextNode from 0-5 and 12-15 in marks. + * Its important to mark all sequences in a text node at once + * because this function replaces the entire text node; so any + * other references to that text node will no longer be in the DOM. + */ +function markMatches(node, lohis) { + const text = node.nodeValue + + const markFragment = document.createDocumentFragment(); + + let prevHi = 0 + for (const [lo, hi] of lohis) { + markFragment.append( + document.createTextNode(text.slice(prevHi, lo)), + markEl(text.slice(lo, hi + 1)) + ) + prevHi = hi + 1 + } + markFragment.append( + document.createTextNode(text.slice(prevHi, text.length)) + ) + + const parent = node.parentElement + parent?.replaceChild(markFragment, node) + return parent +} + +// Activate ancestor tabs so a search match inside an inactive pane becomes visible. +// When multiple panes in the same tabset contain matches, avoid switching away from +// the currently active pane — the user already sees a match there. +function openAllTabsetsContainingEl(el) { + for (const pane of matchAncestors(el, '.tab-pane')) { + const tabContent = pane.closest('.tab-content'); + if (!tabContent) continue; + const activePane = tabContent.querySelector(':scope > .tab-pane.active'); + if (activePane?.querySelector('mark')) continue; + const tabButton = document.querySelector(`[data-bs-target="#${pane.id}"]`); + if (tabButton) new bootstrap.Tab(tabButton).show(); + } +} + +function scrollToFirstVisibleMatch(mainEl) { + for (const mark of mainEl.querySelectorAll("mark")) { + const isMarkVisible = matchAncestors(mark, '.tab-pane').every(markTabPane => + markTabPane.classList.contains("active") + ) + if (isMarkVisible) { + mark.scrollIntoView({ behavior: "smooth", block: "center" }); + return; + } + } +} + +/** + * e.g. + * ```js + * const m = new Map() + * + * arrayMapPush(m, 'dog', 'Max') + * console.log(m) // Map { dog->['Max'] } + * + * arrayMapPush(m, 'dog', 'Samba') + * arrayMapPush(m, 'cat', 'Scruffle') + * console.log(m) // Map { dog->['Max', 'Samba'], cat->['Scruffle'] } + * ``` + */ +const arrayMapPush = (map, key, item) => { + if (!map.has(key)) map.set(key, []) + map.set(key, [...map.get(key), item]) +} + +// copy&paste any string from a quarto page and +// this should find that string in the page and highlight it. +// exception: text that starts outside/inside a tabset and ends +// inside/outside that tabset. +function highlight(searchStr, el) { + const matches = searchMatches(searchStr, el); + + const matchesGroupedByNode = new Map() + for (const match of matches) { + for (const [mel, { lo, hi }] of match) { + arrayMapPush(matchesGroupedByNode, mel, [lo, hi]) + } + } + + for (const [node, lohis] of matchesGroupedByNode) { + markMatches(node, lohis) + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/rfaR-book/_quarto.yml b/rfaR-book/_quarto.yml new file mode 100644 index 0000000..86848da --- /dev/null +++ b/rfaR-book/_quarto.yml @@ -0,0 +1,49 @@ +project: + type: book + output-dir: _book + +book: + title: "rfaR: Reservoir Frequency Analysis in R" + subtitle: "User and Technical Manual" + author: + - name: "Dan McGraw, P.E." + - name: "Andrew Verdin, PhD, P.H." + - name: "C. Haden Smith, P.E." + date: today + repo-url: https://github.com/USACE-RMC/rfaR + repo-actions: [edit, issue] + page-footer: + left: "USACE Risk Management Center" + right: "Built with Quarto" + chapters: + - index.qmd + - 01-introduction.qmd + - 02-methodology.qmd + - 03-installation.qmd + - 04-workflow.qmd + - 05-function-reference.qmd + - 06-simulation-modes.qmd + - 07-validation.qmd + sidebar: + style: docked + background: light + cover-image: cover.png + image: cover.png + +bibliography: references.bib + +format: + html: + theme: + light: flatly + dark: darkly + toc: true + toc-depth: 3 + number-sections: true + code-fold: true + code-tools: true + pdf: + documentclass: scrreprt + toc: true + number-sections: true + colorlinks: true diff --git a/rfaR-book/cover.jpg b/rfaR-book/cover.jpg new file mode 100644 index 0000000000000000000000000000000000000000..f42d2d65d307116dbd70dcbad2cabed00e3db4fb GIT binary patch literal 120849 zcmb3fI$HYM 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