From c6d3e75c3ea2ed69fbc23731cecfaa4b70cd5020 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Wed, 20 Jul 2016 14:26:57 -0400 Subject: [PATCH 01/25] Add inferelator R implementation for comparison testing --- Inferelator/README | 132 + Inferelator/R_scripts/bayesianRegression.R | 433 ++ Inferelator/R_scripts/design_and_response.R | 456 ++ Inferelator/R_scripts/dream8_out.R | 35 + Inferelator/R_scripts/evaluate.R | 54 + Inferelator/R_scripts/group_predictors.R | 121 + Inferelator/R_scripts/men.R | 555 ++ Inferelator/R_scripts/mi_and_clr.R | 122 + Inferelator/R_scripts/net_report.Rmd | 240 + Inferelator/R_scripts/net_report_new.R | 20 + Inferelator/R_scripts/priors.R | 164 + Inferelator/R_scripts/summarize_results.R | 134 + Inferelator/R_scripts/tfa.R | 140 + Inferelator/R_scripts/utils.R | 115 + Inferelator/R_scripts/vis_res_tfa.R | 87 + Inferelator/R_scripts/vis_tfs_and_targets.R | 230 + Inferelator/inferelator.R | 448 ++ Inferelator/input/.DS_Store | Bin 0 -> 6148 bytes Inferelator/input/bsubtilis/.DS_Store | Bin 0 -> 6148 bytes Inferelator/input/bsubtilis/expression.tsv | 4219 ++++++++++++++ Inferelator/input/bsubtilis/gold_standard.tsv | 4892 +++++++++++++++++ Inferelator/input/bsubtilis/meta_data.tsv | 270 + Inferelator/input/bsubtilis/tf_names.tsv | 239 + Inferelator/input/dream4/expression.tsv | 101 + Inferelator/input/dream4/gold_standard.tsv | 101 + Inferelator/input/dream4/meta_data.tsv | 422 ++ Inferelator/input/dream4/tf_names.tsv | 100 + Inferelator/input/ecoli/expression.tsv | 4512 +++++++++++++++ Inferelator/input/ecoli/gold_standard.tsv | 4512 +++++++++++++++ Inferelator/input/ecoli/meta_data.tsv | 488 ++ Inferelator/input/ecoli/tf_names.tsv | 334 ++ Inferelator/jobs/bsubtilis_bbsr_high.R | 25 + Inferelator/jobs/bsubtilis_bbsr_low.R | 25 + Inferelator/jobs/bsubtilis_bbsr_noprior.R | 25 + Inferelator/jobs/bsubtilis_men_high.R | 25 + Inferelator/jobs/bsubtilis_men_low.R | 25 + Inferelator/jobs/bsubtilis_men_noprior.R | 25 + Inferelator/jobs/dream4_bbsr_high.R | 24 + Inferelator/jobs/dream4_bbsr_low.R | 24 + Inferelator/jobs/dream4_bbsr_noprior.R | 24 + Inferelator/jobs/dream4_men_high.R | 25 + Inferelator/jobs/dream4_men_low.R | 24 + Inferelator/jobs/dream4_men_noprior.R | 24 + Inferelator/jobs/dream4_simplified.R | 24 + Inferelator/jobs/ecoli_bbsr_high.R | 25 + Inferelator/jobs/ecoli_bbsr_low.R | 25 + Inferelator/jobs/ecoli_bbsr_noprior.R | 25 + Inferelator/jobs/ecoli_men_high.R | 25 + Inferelator/jobs/ecoli_men_low.R | 25 + Inferelator/jobs/ecoli_men_noprior.R | 25 + 50 files changed, 24145 insertions(+) create mode 100644 Inferelator/README create mode 100644 Inferelator/R_scripts/bayesianRegression.R create mode 100644 Inferelator/R_scripts/design_and_response.R create mode 100644 Inferelator/R_scripts/dream8_out.R create mode 100644 Inferelator/R_scripts/evaluate.R create mode 100644 Inferelator/R_scripts/group_predictors.R create mode 100644 Inferelator/R_scripts/men.R create mode 100644 Inferelator/R_scripts/mi_and_clr.R create mode 100644 Inferelator/R_scripts/net_report.Rmd create mode 100644 Inferelator/R_scripts/net_report_new.R create mode 100644 Inferelator/R_scripts/priors.R create mode 100644 Inferelator/R_scripts/summarize_results.R create mode 100644 Inferelator/R_scripts/tfa.R create mode 100644 Inferelator/R_scripts/utils.R create mode 100644 Inferelator/R_scripts/vis_res_tfa.R create mode 100644 Inferelator/R_scripts/vis_tfs_and_targets.R create mode 100644 Inferelator/inferelator.R create mode 100644 Inferelator/input/.DS_Store create mode 100644 Inferelator/input/bsubtilis/.DS_Store create mode 100644 Inferelator/input/bsubtilis/expression.tsv create mode 100644 Inferelator/input/bsubtilis/gold_standard.tsv create mode 100644 Inferelator/input/bsubtilis/meta_data.tsv create mode 100644 Inferelator/input/bsubtilis/tf_names.tsv create mode 100644 Inferelator/input/dream4/expression.tsv create mode 100644 Inferelator/input/dream4/gold_standard.tsv create mode 100644 Inferelator/input/dream4/meta_data.tsv create mode 100644 Inferelator/input/dream4/tf_names.tsv create mode 100644 Inferelator/input/ecoli/expression.tsv create mode 100644 Inferelator/input/ecoli/gold_standard.tsv create mode 100644 Inferelator/input/ecoli/meta_data.tsv create mode 100644 Inferelator/input/ecoli/tf_names.tsv create mode 100644 Inferelator/jobs/bsubtilis_bbsr_high.R create mode 100644 Inferelator/jobs/bsubtilis_bbsr_low.R create mode 100644 Inferelator/jobs/bsubtilis_bbsr_noprior.R create mode 100644 Inferelator/jobs/bsubtilis_men_high.R create mode 100644 Inferelator/jobs/bsubtilis_men_low.R create mode 100644 Inferelator/jobs/bsubtilis_men_noprior.R create mode 100644 Inferelator/jobs/dream4_bbsr_high.R create mode 100644 Inferelator/jobs/dream4_bbsr_low.R create mode 100644 Inferelator/jobs/dream4_bbsr_noprior.R create mode 100644 Inferelator/jobs/dream4_men_high.R create mode 100644 Inferelator/jobs/dream4_men_low.R create mode 100644 Inferelator/jobs/dream4_men_noprior.R create mode 100644 Inferelator/jobs/dream4_simplified.R create mode 100644 Inferelator/jobs/ecoli_bbsr_high.R create mode 100644 Inferelator/jobs/ecoli_bbsr_low.R create mode 100644 Inferelator/jobs/ecoli_bbsr_noprior.R create mode 100644 Inferelator/jobs/ecoli_men_high.R create mode 100644 Inferelator/jobs/ecoli_men_low.R create mode 100644 Inferelator/jobs/ecoli_men_noprior.R diff --git a/Inferelator/README b/Inferelator/README new file mode 100644 index 0000000..3464172 --- /dev/null +++ b/Inferelator/README @@ -0,0 +1,132 @@ +NOTE: + +This directory contains a subset of https://github.com/ChristophH/Inferelator +for testing and comparison purposes. + +Call the inferelator script from the base directory (the one containing this +README) with a job config file as argument. + +Example call: Rscript inferelator.R jobs/dream4_cfg.R + + + +-------------------------------------------------------------------------------- +Default parameters and a brief explanation of each one +-------------------------------------------------------------------------------- + +PARS$input.dir <- 'input/dream4' # path to the input files + +PARS$exp.mat.file <- 'expression.tsv' # required; see definition below +PARS$tf.names.file <- 'tf_names.tsv' # required; see definition below +PARS$meta.data.file <- 'meta_data.tsv' # assume all steady state if NULL +PARS$priors.file <- 'gold_standard.tsv' # no priors if NULL +PARS$gold.standard.file <- 'gold_standard.tsv' # no evaluation if NULL +PARS$leave.out.file <- NULL # file with list of conditions that will be ignored +PARS$randomize.expression <- FALSE # whether to scramble input expression + +PARS$job.seed <- 42 # random seed; can be NULL +PARS$save.to.dir <- file.path(PARS$input.dir, date.time.str) # output directory +PARS$num.boots <- 20 # number of bootstraps; no bootstrapping with a value of 1 +PARS$max.preds <- 10 # max number of predictors based on CLR to pass to model + # selection method +PARS$mi.bins <- 10 # number of bins to use for mutual information calculation +PARS$cores <- 8 # number of cpu cores + +PARS$delT.max <- 110 # max number of time units allowed between time series + # conditions +PARS$delT.min <- 0 # min number of time units allowed between time series + # conditions +PARS$tau <- 45 # constant related to half life of mRNA (see Core model) + +PARS$perc.tp <- 0 # percent of true priors that will be used; can be vector +PARS$perm.tp <- 1 # number of permutations of true priors +PARS$perc.fp <- 0 # percent of false priors (100 = as many false priors as + # there are true priors); can be vector +PARS$perm.fp <- 1 # number of permutations of false priors +PARS$pr.sel.mode <- 'random' # prior selection mode: 'random' or 'tf' + # if 'random', the true priors are randomly chosen + # from all priors edges, if 'tf', + # PARS$perc.tp is interpreted as the percent of + # TFs to use for true priors and all interactions + # for the chosen TFs will be used + +PARS$eval.on.subset <- FALSE # whether to evaluate only on the part of the + # network that has connections in the gold + # standard; if TRUE false priors will only be + # drawn from that part of the network + +PARS$method <- 'BBSR' # which method to use; either 'MEN' or 'BBSR' +PARS$prior.weight <- 1 # the weight for the priors; has to be larger than 1 + # for priors to have an effect + +PARS$use.tfa <- FALSE # whether to estimate transcription factor activities and + # use those in the regression models + # if TRUE, interactions in priors file shoud be signed, + # i.e. -1 for repression and +1 for activation +PARS$prior.ss <- FALSE # whether to also sub-sample from the prior matrix during + # each bootstrap; if TRUE, priors are sampled randomly with + # replacement; if FALSE, all priors are used as is + +PARS$output.summary <- TRUE # write a summary tsv and RData file of network + +PARS$output.report <- TRUE # create html network report + +PARS$output.tf.plots <- TRUE # create png files with plots of TFs and targets + +-------------------------------------------------------------------------------- +Required Input Files +-------------------------------------------------------------------------------- + +expression.tsv +-------------- +expression values; must include row (genes) and column (conditions) names + +tf_names.tsv +------------ +one TF name on each line; must be subset of the row names of the expression data + + + +-------------------------------------------------------------------------------- +Optional Input Files +-------------------------------------------------------------------------------- + +meta_data.tsv +------------- +the meta data describing the conditions; must include column names; +has five columns: +isTs: TRUE if the condition is part of a time-series, FALSE else +is1stLast: "e" if not part of a time-series; "f" if first; "m" middle; "l" last +prevCol: name of the preceding condition in time-series; NA if "e" or "f" +del.t: time in minutes since prevCol; NA if "e" or "f" +condName: name of the condition + +priors.tsv +---------- +matrix of 0 and 1 indicating whether we have prior knowledge in +the interaction of one TF and a gene; one row for each gene, one column for +each TF; must include row (genes) and column (TF) names + +gold_standard.tsv +----------------- +needed for validation; matrix of 0 and 1 indicating whether there is an +interaction between one TF and a gene; one row for each gene, one column for +each TF; must include row (genes) and column (TF) names + + + +-------------------------------------------------------------------------------- +Output Files +-------------------------------------------------------------------------------- + +One or more betas_frac_tp_X_perm_X--frac_fp_X_perm_X_X.RData files. One file +per true and false prior and prior weight combination. Each RData file contains +two lists of length PARS$num.boots where every entry is a matrix of betas and +confidence scores (rescaled betas) respectively. + +One or more combinedconf_frac_tp_X_perm_X--frac_fp_X_perm_X_X.RData files with +one matrix each. The matrix is the rank-combined version of the confidence +scores of all bootstraps. + +A params_and_input.RData file with data objects holding the user set parameters, +and input and input-derived objects. diff --git a/Inferelator/R_scripts/bayesianRegression.R b/Inferelator/R_scripts/bayesianRegression.R new file mode 100644 index 0000000..2a2c311 --- /dev/null +++ b/Inferelator/R_scripts/bayesianRegression.R @@ -0,0 +1,433 @@ +# TODO: Add comment +# +# Author: Christoph +############################################################################### + +BBSR <- function(X, Y, clr.mat, nS, no.pr.val, weights.mat, prior.mat, cores) { + + # Scale and permute design and response matrix + X <- t(scale(t(X))) + Y <- t(scale(t(Y))) + + G <- nrow(Y) # number of genes + genes <- rownames(Y) + K <- nrow(X) # max number of possible predictors (number of TFs) + tfs <- rownames(X) + + weights.mat <- weights.mat[genes, tfs] + clr.mat <- clr.mat[genes, tfs] + prior.mat <- prior.mat[genes, tfs] + + pp <- matrix(FALSE, G, K, dimnames=list(genes, tfs)) # predictors that will be used in the regression + + # keep all predictors that we have priors for + pp[(weights.mat != no.pr.val) & !is.na(clr.mat)] <- TRUE + pp[(prior.mat != 0) & !is.na(clr.mat)] <- TRUE + + # for each gene, add the top nS predictors of the list to possible predictors + clr.mat[clr.mat == 0] <- NA + for (ind in 1:G) { + clr.order <- order(clr.mat[ind, ], decreasing=TRUE, na.last=NA) + pp[ind, clr.order[1:min(K, nS, length(clr.order))]] <- TRUE + } + # remove self-interactions from list of potential interactions + preds <- intersect(genes, tfs) + diag(pp[preds, preds]) <- FALSE + + # NEW: remove predictors that are constant or essentially duplicates of others + #const <- which(apply(is.na(X), 1, sum) > 0) + #cat('The following predictors are constant and will not be used:\n') + #print(const) + #pp[, const] <- FALSE + #exp.mat <- X + #done <- FALSE + #while (!done) { + # cor.mat <- cor(t(exp.mat)) + # diag(cor.mat) <- 0 + # cor.mat[is.na(cor.mat)] <- 0 + # todo <- which(apply(cor.mat > 0.99, 1, sum) > 0) + # if (length(todo) > 0) { + # remove <- which(cor.mat[todo[1], ] > 0.99)[1] + # cat('Removing predictor', rownames(exp.mat)[remove], 'because it has correlation > 0.99 with', rownames(exp.mat)[todo[1]], '\n') + # pp[, rownames(exp.mat)[todo[1]]] <- pp[, rownames(exp.mat)[todo[1]]] | pp[, rownames(exp.mat)[remove]] + # pp[, rownames(exp.mat)[remove]] <- FALSE + # exp.mat <- exp.mat[-remove, ] + # } else { + # done <- TRUE + # } + #} + + out.list <- mclapply(1:G, BBSRforOneGene, X, Y, pp, weights.mat, nS, mc.cores=cores) + return(out.list) +} + +BBSRforOneGene <- function(ind, X, Y, pp, weights.mat, nS) { + if (ind %% 100 == 0) { + cat('Progress: BBSR for gene', ind, '\n') + } + + pp.i <- pp[ind, ] + + if (sum(pp.i) == 0) { + return(list(ind=ind, pp=rep(TRUE, length(pp.i)), betas=0, betas.resc=0)) + } + + # create BestSubsetRegression input + y <- as.vector(Y[ind, ], mode="numeric") + x <- t(matrix(X[pp.i, ], ncol=ncol(X))) + g <- matrix(weights.mat[ind, pp.i], ncol=sum(pp.i)) + + # experimental stuff + spp <- ReduceNumberOfPredictors(y, x, g, nS) + pp.i[pp.i == TRUE] <- spp + x <- t(matrix(X[pp.i, ], ncol=ncol(X))) + g <- matrix(weights.mat[ind, pp.i], ncol=sum(pp.i)) + + betas <- BestSubsetRegression(y, x, g) + betas.resc <- PredErrRed(y, x, betas) + + return(list(ind=ind, pp=pp.i, betas=betas, betas.resc=betas.resc)) +} + +CallBestSubSetRegression <- function(ind, Xs, Y, Pi, clr.mat, nS, + no.pr.val = NULL, + weights.mat = NULL) { + # Calls best subset regression + # TODO: Add more here + + cat("*") + browser() + # Scale and permute design and response matrix + Xs.scaled <- t(scale(t(Xs[, Pi[ind, ]]))) + Y.scaled <- t(scale(t(Y[, Pi[ind, ]]))) + + K <- nrow(Xs.scaled) # max number of possible predictors + + pp <- rep(FALSE, K) # predictors that will be used in the regression + + # if a clr matrix is given + if (is.null(dim(clr.mat)) == FALSE) { + + # bump predictors with priors to top of list; not needed anymore + #clr.mat[ind, weights.mat[ind, ] != no.pr.val] <- clr.mat[ind, weights.mat[ind, ] != no.pr.val] + max(clr.mat) + .Machine$double.eps + + # send self predictor to end of list if gene we are inferring on is a TF + # also remove as prior if present + if (ind <= K) { + clr.mat[ind, ind] <- -Inf + weights.mat[ind, ind] <- no.pr.val + } + + clr.order <- order(clr.mat[ind, ], decreasing=TRUE) + have.priors <- which(weights.mat[ind, ] != no.pr.val) + + # potential predictors are the union of predictors with priors and the top + # nS ones based on clr matrix + pp[unique(c(have.priors, clr.order[1:min(K, nS)]))] <- TRUE + + } + else { + + pp <- rep(TRUE, K) + if (ind <= K) { + pp[ind] <- FALSE + } + + } + + #cat("\n", length(pp), "\n") + # create BestSubsetRegression input + y <- as.vector(Y.scaled[ind, ], mode="numeric") + x <- t(as.matrix(Xs.scaled[pp, ])) + g <- weights.mat[ind, pp] + + # experimental stuff + spp <- ReduceNumberOfPredictors(y, x, g, min(K, nS)) + pp[pp == TRUE] <- spp + x <- t(as.matrix(Xs.scaled[pp, ])) + g <- weights.mat[ind, pp] + + # run the best subset regression + #Rprof("bssr_new.out") + betas.x <- BestSubsetRegression(y, x, g) + #betas.x <- BayesianModelAveraging(y, x, g) + #Rprof(NULL) + #betas.full <- rep(0, K) + #betas.full[pp] <- betas.x + #names(betas.full) <- rownames(Xs) + + # rescale betas based on amount of error reduction by the predictors + betas.resc <- PredErrRed(y, x, betas.x) + #betas.resc.full <- rep(0, K) + #betas.resc.full[pp] <- betas.resc + #names(betas.resc.full) <- rownames(Xs) + + # cut the crap and end this here + return(list(ind=ind, pp=pp, betas=betas.x, betas.resc=betas.resc)) + +} + + +ReduceNumberOfPredictors <- function(y, x, g, n) { + K <- ncol(x) + spp <- + if (K <= n) { + return(rep(TRUE, K)) + } + + combos <- cbind(diag(K) == 1, CombCols(diag(K))) + bics <- ExpBICforAllCombos(y, x, g, combos) + bics.avg <- apply(t(t(combos) * bics), 1, sum) + ret <- rep(FALSE, K) + ret[order(bics.avg)[1:n]] <- TRUE + + return(ret) +} + +BayesianModelAveraging <- function(y, x, g) { + + mprior.size <- g + bms.out <- bms(cbind(y,x), nmodel=0, mprior='pip', + mprior.size=mprior.size, user.int=F) + #bms.out <- bms(cbind(y,x), nmodel=0, user.int=F) + + tmp <- coef(bms.out) + tmp <- tmp[order(tmp[, 'Idx']), ] + + ret <- tmp[, 'Post Mean'] + ret[tmp[, 'PIP'] < 0.9] <- 0 + #print(sum(tmp[, 'PIP'] > 0.5)) + #return(as.numeric(tmp[, 'PIP'] > 0.9)[order(tmp[, 'Idx'])]) + return(ret) +} + +BestSubsetRegressionAllWeights <- function(y, x, g){ + #Do best subset regression without any weight (if this option is chosen) + #and with the combination of all weights that are chosen + #return the results in a list + + # Q CH 11|18|2011: Is this ever going to be used? +} + +BestSubsetRegression <- function(y, x, g) { + # Do best subset regression by using all possible combinations of columns of + # x as predictors of y. Model selection criterion is BIC using results of + # Bayesian regression with Zellner's g-prior. + # + # Args: + # y: dependent variable + # x: independent variable + # g: value for Zellner's g-prior; can be single value or vector + # + # Returns: + # Beta vector of best model + + K <- ncol(x) + N <- nrow(x) + ret <- c() + + combos <- AllCombinations(K) + bics <- ExpBICforAllCombos(y, x, g, combos) + + not.done <- TRUE + while (not.done) { + best <- which.min(bics) + #cat('best is', best, '\n') + + # For the return value, re-compute beta ignoring g-prior. + betas <- rep(0, K) + if (best > 1) { + x.tmp <- matrix(x[,combos[, best]], N) + + tryCatch({ + bhat <- solve(crossprod(x.tmp), crossprod(x.tmp, y)) + betas[combos[, best]] <- bhat + not.done <- FALSE + }, error = function(e) { + if (any(grepl('solve.default', e$call)) & grepl('singular', e$message)) { + # error in solve - system is computationally singular + cat(bics[best], 'at', best, 'replaced\n') + bics[best] <<- Inf + } else { + stop(e) + } + }) + } + else { + not.done <- FALSE + } + } + return(betas) +} + + +AllCombinations <- function(k) { + # Create a boolean matrix with all possible combinations of 1:k. + # Output has k rows and 2^k columns where each column is one combination. + # Note that the first column is all FALSE and corresponds to the null model. + if (k < 1) { + stop("No combinations for k < 1") + } + + N <- 2^k + out <- matrix(FALSE, k, N) + out[1, 2] <- TRUE + + row <- 2 + col <- 3 + while (col < N) { + out[row, col] <- TRUE + + for (i in 1:(col-2)) { + out[, col + i] <- out[, col] | out[, i + 1] + } + + row <- row + 1 + col <- col * 2 - 1 + } + + return(out) +} + + +CombCols <- function(m) { + K <- ncol(m) + ret <- matrix(TRUE, nrow(m), K * (K - 1) / 2) + ret.col <- 1 + for (i in 1:(K - 1)) { + for (j in (i + 1):K) { + ret[, ret.col] <- m[, i] | m[, j] + ret.col <- ret.col + 1 + } + } + return(ret) +} + + +ExpBICforAllCombos <- function(y, x, g, combos) { + # For a list of combinations of predictors do Bayesian linear regression, + # more specifically calculate the parametrization of the inverse gamma + # distribution that underlies sigma squared using Zellner's g-prior method. + # Parameter g can be a vector. The expected value of the log of sigma squared + # is used to compute expected values of BIC. + # Returns list of expected BIC values, one for each model. + K <- ncol(x) + N <- nrow(x) + + C <- ncol(combos) + bics <- rep(0, C) + + # is the first combination the null model? + first.combo <- 1 + if (sum(combos[, 1]) == 0) { + bics[1] <- N * log(var(y)) + first.combo <- 2 + } + + # shape parameter for the inverse gamma sigma squared would be drawn from + shape <- N / 2 + + # compute digamma of shape here, so we can re-use it later + dig.shape <- digamma(shape) + + # pre-compute the crossproducts that we will need to solve for beta + xtx <- crossprod(x) + xty <- crossprod(x, y) + + # In Zellner's formulation there is a factor in the calculation of the rate + # parameter: 1 / (g + 1) + # Here we replace the factor with the approriate matrix since g is a vector + # now. + var.mult <- matrix(sqrt(1 / (g + 1)), K, K) + var.mult <- var.mult * t(var.mult) + + for (i in first.combo:C){ + comb <- combos[, i] + x.tmp <- matrix(x[, comb], N) + k <- sum(comb) + + tryCatch({ + # this is faster than calling lm + bhat <- solve(xtx[comb, comb], xty[comb]) + + ssr <- sum((y - x.tmp %*% bhat)^2) # sum of squares of residuals + + # rate parameter for the inverse gamma sigma squared would be drawn from + # our guess on the regression vector beta is all 0 for sparse models + rate <- (ssr + + (0 - t(bhat)) %*% + (xtx[comb, comb] * var.mult[comb, comb]) %*% + t(0 - t(bhat))) / 2 + + # the expected value of the log of sigma squared based on the + # parametrization of the inverse gamma by rate and shape + exp.log.sigma2 <- log(rate) - dig.shape + + # expected value of BIC + bics[i] <- N * exp.log.sigma2 + k * log(N) + + }, error = function(e) { + if (any(grepl('solve.default', e$call)) & grepl('singular', e$message)) { + # error in solve - system is computationally singular + bics[i] <<- Inf + } else { + stop(e) + } + }) + + } + + return(bics) +} + + + +PredErrRed <- function(y, x, beta) { + # Calculates the error reduction (measured by variance of residuals) of each + # predictor - compare full model to model without that predictor + N <- nrow(x) + K <- ncol(x) + pred <- beta != 0 + P <- sum(pred) + + # compute sigma^2 for full model + residuals <- y - x %*% beta + sigma.sq.full <- var(residuals) + + # this will be the output + err.red <- rep(0, K) + + # special case if there is only one predictor + if (P == 1) { + err.red[pred] <- 1 - sigma.sq.full / var(y) + return(err.red) + } + + # one by one leave out each predictor and re-compute the model with the + # remaining ones + for (i in (1:K)[pred]) { + pred.tmp <- pred + pred.tmp[i] <- FALSE + x.tmp <- matrix(x[,pred.tmp], N, P-1) + #bhat <- solve(t(x.tmp) %*% x.tmp) %*% t(x.tmp) %*% y + tryCatch({ + bhat <- solve(crossprod(x.tmp), crossprod(x.tmp, y)) + }, error = function(e) { + if (any(grepl('solve.default', e$call)) & grepl('singular', e$message)) { + # error in solve - system is computationally singular + cat('PredErrRed: error in solve - system is computationally singular\n') + browser() + stop(e) + } else { + stop(e) + } + }) + residuals <- y - x.tmp %*% bhat + sigma.sq <- var(residuals) + + err.red[i] <- 1 - sigma.sq.full / sigma.sq + } + return(err.red) +} + + diff --git a/Inferelator/R_scripts/design_and_response.R b/Inferelator/R_scripts/design_and_response.R new file mode 100644 index 0000000..46be720 --- /dev/null +++ b/Inferelator/R_scripts/design_and_response.R @@ -0,0 +1,456 @@ +## .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. +## /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ / / \ \ / / \ \ +##`-' `-`-' `-`-' `-`-' `-`-' `-`-' `-`-' `-`-' ' ' +# input: cM - colMap +# r - expression matrix (assumed already normalized) +# param - a vector of 2 params: +# 1- c1 - cutoff1: represent the maximal time interval allowed for time series (ts) data +# 2- 'trivial' or 'time_delayed' (param to choose the type of design matrix) +# 3- 'consecutive' or 'all_intervals' (determine if consecutive time measurements [no longer than c1], +# or all permutations of time measurements [up to c1] respectively) +# 4- 'TRUE' or 'FALSE' use_t0_as_steady_state +# 5- 'TRUE' or 'FALSE' use_delt_bigger_than_cutoff_as_steady_state +# +# output: +# steadyStateDesignMat, timeSeriesDesignMat + +get_usr_chosen_design <- function(cM, r, delT_min, delT_max, time_delayed, + all_intervals, use_t0_as_steady_state, + use_delt_bigger_than_cutoff_as_steady_state) { + + delT_min_vec = rep(delT_min, nrow(cM)) + delT_max_vec = rep(delT_max, nrow(cM)) + + delT_vec = cM$del.t + isTs_vec = cM$isTs + eq_idx = which(!isTs_vec) + ts_idx = which(isTs_vec) + + delT_vec = delT_vec [ts_idx] + delT_min_vec = delT_min_vec[ts_idx] + delT_max_vec = delT_max_vec[ts_idx] + # set delT_vec: + # 0 - last time measurement in ts + # >0 - first and middle time measurements in ts + # following line make 0s indicate first time measurement in ts + delT_vec[which(is.na(delT_vec))] = 0 + + delT_vec_trivial = delT_vec + # following 2 lines make 0s indicate last time measurement in ts + delT_vec[-length(delT_vec)] = delT_vec[-1] + delT_vec[length(delT_vec)] = 0 + + # data for steady state + rSS = r[,eq_idx] + # if we have time series experiments + if(length(ts_idx)>0) { + # data for time series + if( length(eq_idx) > 0 ){ #simple check to see if there are any equlibrium conditions + rTS = r[,-eq_idx] + }else{ + rTS = r + } + + eq_idx_pseudo = numeric() + + # get ts starting conditions, we treat these as equilibrium + if (use_t0_as_steady_state) + eq_idx_pseudo = which(delT_vec_trivial == 0) + + # get ts conditions with larger than c1 delt, we treat these as equilibrium + if (use_delt_bigger_than_cutoff_as_steady_state) + eq_idx_pseudo = c(eq_idx_pseudo, which(delT_vec_trivial > max(delT_max_vec))) + + # create design matrix for steady state + if( length(eq_idx) > 0 ){ + DesignMatSS = cbind(rSS, rTS[,eq_idx_pseudo]) + }else{ + DesignMatSS = rTS[,eq_idx_pseudo,drop=F] + } + + if (all_intervals) { # all permutations time series + #x = get_all_perms(delT_vec, delT_min_vec[1], delT_max_vec[1]) #assumes delT_min & max are scalars + x = get_all_perms_vec(delT_vec, delT_min_vec, delT_max_vec) #assumes delT_min & max are vectors + init_ind = x[[1]] + boundary_ind = x[[2]] + DesignMatTS = rTS[,init_ind] + } else { # consecutive time series + if(time_delayed) { + DesignMatTS = rTS[,which(delT_vec != 0 & delT_vec <= delT_max_vec)] + } else { + DesignMatTS = rTS[,which(delT_vec_trivial != 0 & delT_vec_trivial <= delT_max_vec)] + } + } + } else { + DesignMatSS = rSS + DesignMatTS = matrix(0, nrow(DesignMatSS), 0) + } + return(list(DesignMatSS,as.matrix(DesignMatTS))) +} + + +## .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. +## /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ / / \ \ / / \ \ +##`-' `-`-' `-`-' `-`-' `-`-' `-`-' `-`-' `-`-' ' ' +# input: cM - colMap +# r - ratios matrix (assumed already normalized) +# param - a vector of 5 params: +# 1- c1 - cutoff1: represent the maximal time interval allowed for time series (ts) data +# 2- 'trivial','time_difference','rate','inf_1', or 'inf_1_all_intervals' +# 3- tau +# 4- TRUE or FALSE use_t0_as_steady_state +# 5- TRUE or FALSE use_delt_bigger_than_cutoff_as_steady_state +# +# output: +# steadyStateResponseMat timeSeriesResponseMat + +get_usr_chosen_response <- function(cM, r, delT_min, delT_max, method, tau, + use_t0_as_steady_state, + use_delt_bigger_than_cutoff_as_steady_state) { + + delT_min_vec = rep(delT_min, nrow(cM)) + delT_max_vec = rep(delT_max, nrow(cM)) + + delT_vec = cM$del.t + isTs_vec = cM$isTs + eq_idx = which(!isTs_vec) + ts_idx = which(isTs_vec) + + delT_vec = delT_vec[ts_idx] + delT_min_vec = delT_min_vec[ts_idx] + delT_max_vec = delT_max_vec[ts_idx] + +# set delT_vec: +# 0 - last time measurement in ts +# >0 - first and middle time measurements in ts +# following line make 0's indicate first time measurement in ts + delT_vec[which(is.na(delT_vec))] = 0 + delT_vec_trivial = delT_vec +# following 2 lines make 0's indicate last time measurement in ts + delT_vec[-length(delT_vec)] = delT_vec[-1] + delT_vec[length(delT_vec)] = 0 + + rSS = r[,eq_idx] + # if we have time series experiments + if(length(ts_idx)>0) { + if(any(eq_idx)){ + rTS = r[,-eq_idx] + }else{ + rTS=r + } + + eq_idx_pseudo = numeric() + # get ts starting conditions, we treat these as equilibrium + if (use_t0_as_steady_state) + eq_idx_pseudo = which(delT_vec_trivial == 0) + + # get ts conditions with larger than c1 delt, we treat these as equilibrium + if (use_delt_bigger_than_cutoff_as_steady_state) + eq_idx_pseudo = c(eq_idx_pseudo, which(delT_vec_trivial > max(delT_max_vec))) + + if(any(eq_idx)){ + response_matrixSS = cbind(rSS, rTS[,eq_idx_pseudo]) + }else{ + if(any(eq_idx_pseudo)){ + response_matrixSS = rTS[,eq_idx_pseudo,drop=F] + }else{ + response_matrixSS = rSS + } + } + + init_ind = which(delT_vec != 0 & delT_vec <= delT_max_vec) + boundary_ind = init_ind+1 + + # finished response matrices now go on to response + if (method == 'trivial') { + response_matrixTS = rTS[,boundary_ind] + } else if (method == 'time_difference') { + response_matrixTS = (rTS[,boundary_ind] - rTS[,init_ind]) + } else if (method == 'rate') { + response_matrixTS = t(1/delT_vec[init_ind] * t(rTS[,boundary_ind] - rTS[,init_ind])) + } else if (method == 'inf_1') { + response_matrixTS = t(tau/delT_vec[init_ind] * t(rTS[,boundary_ind] - rTS[,init_ind])) + (rTS[,init_ind]) + } else if (method == 'inf_1_all_intervals') { + #x = get_all_perms (delT_vec, delT_min_vec[1], delT_max_vec[1]) #assumes delT_min and delT_max are scalars + x = get_all_perms_vec(delT_vec, delT_min_vec, delT_max_vec) #assumes delT_min and delT_max are vectors + init_ind = x[[1]] + boundary_ind = x[[2]] + response_matrixTS = t(tau/delT_vec[init_ind] * t(rTS[,boundary_ind] - rTS[,init_ind])) + (rTS[,init_ind]) + } else { + stop("unknown response read") + } + } else { + response_matrixSS = rSS + response_matrixTS = matrix(0, nrow(response_matrixSS), 0) + } + return(list(response_matrixSS ,response_matrixTS)) +} + + + +## .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. .-.-. +## /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ /|/ \|\ / / \ \ / / \ \ +##`-' `-`-' `-`-' `-`-' `-`-' `-`-' `-`-' `-`-' ' ' +# input: +# 1- dMSS: design matrix steady state +# 2- dMTS: design matrix time series +# 3- rMSS: response matrix steady state +# 4- dMTS: response matrix time series +# 5- param: what final design matrix? choose from all, ts, or ss + +# output: +# resopnse and corresponding design matrices +make_final_design_and_response_matrix <- function(dMSS, dMTS, rMSS, rMTS, param, cS, r, tf.names, make.des.red.exp = F) { + if (param == 'all') { + final_response_matrix = cbind(rMSS, rMTS) + final_design_matrix = cbind(dMSS, dMTS) + } else if (param == 'ts') { + final_response_matrix = rMTS + final_design_matrix = dMTS + } else if (param == 'ss') { + final_response_matrix = rMSS + final_design_matrix = dMSS + } else { + stop("unknown final design or response matrices read") + } + + #check if we have biclusters + #if we do, make final_response_martix to be the + #median of the genes in the bicluster, and add TFs to the end of that + + cs.num.rows <- as.vector(unlist(lapply(cS, function(i) i$nrows))) + if(any(cs.num.rows > 1) & (nrow(r) != length(cS))){ + cat("We have biclusters--generating redExp for response and design--\n") + #finding if we have any single genesin the clusterStack + #also adding any genes that are not in clusterStack as singletons + singletons <- unlist(lapply( cS[ which(cs.num.rows == 1) ], function(i) i$rows)) + ag <- unique(c(unlist(lapply(cS, function(i) i$rows)), tf.names)) + + singletons.to.add <- unique(c(singletons, rownames(r)[which(!rownames(r) %in% ag)])) + #tf.names #hack to keep full list of tfs, used in making the design matrix + if(!is.null(singletons.to.add) & any(singletons.to.add %in% tf.names)) + singletons.to.add <- singletons.to.add[-which(singletons.to.add %in% tf.names)] + + #intialize red.exp, which contains the reduced expression (median expression) + #for reach gene in the cluster + red.exp <- matrix( NA, length(tf.names) + length(cs.num.rows) + length(singletons.to.add), ncol(final_response_matrix)) + colnames(red.exp) <- colnames(final_response_matrix) + + des.red.exp <- matrix( NA, length(tf.names) + length(cs.num.rows) + length(singletons.to.add), ncol(final_response_matrix)) + #initialize resp_idx, + #index of i,j contains a 0 if condition i is not in bicluster + #or the index of j in colnames(final_response_matrix) if condition j is in bicluster i + resp_idx <- matrix( 0, length(tf.names) + length(cs.num.rows) + length(singletons.to.add), ncol(final_response_matrix)) + + cs.start.idx <- length(tf.names) + 1 + cs.end.idx <- cs.start.idx + length(cS) - 1 + mat.idx <- cs.start.idx:cs.end.idx + for(i in 1:length(cS)){ + #get the rows for the bicluster + cs.rows <- cS[[i]]$rows + #get the cols for the bicluster...this if statement is because some biclusters + #have $cols and some have $conds...WTF + if( !is.null(cS[[i]]$cols) ){ + cs.cols <- cS[[i]]$cols + }else{ + cs.cols <- cS[[i]]$conds + } + + #calculate the reduced expression for this bicluster, and put it in red.exp + #NOTE: some biclusters contain the first time-series...this can't be in the + #response matrix, because we use a time lag, so the first time-series is removed + #THUS, we only take the columns which are in the response matrix + cs.cols <- colnames(final_response_matrix)[which(colnames(final_response_matrix) %in% cs.cols)] + cs.cols.idx <- which(colnames(final_response_matrix) %in% cs.cols) + + cs.red.exp <- apply(final_response_matrix[cs.rows, cs.cols, drop=F], 2, mean) + red.exp[mat.idx[i], cs.cols.idx] <- cs.red.exp + + #create the indexing vector for this bicluster and store it in resp_idx + all.idx <- rep(0, ncol(final_response_matrix)) + all.idx[ which(colnames(final_response_matrix) %in% cs.cols) ] <- which(colnames(final_response_matrix) %in% cs.cols) + resp_idx[mat.idx[i], ] <- all.idx + + #getting the red_exp for the design matrix + if(make.des.red.exp) + des.red.exp[mat.idx[i],] <- apply( final_design_matrix[cs.rows, ,drop=F],2,mean) + } + #add the genes which are not in the any clusters to the clusterStack + sing.start.idx <- length(tf.names) + 1 + length(cS) + sing.stop.idx <- sing.start.idx + length(singletons.to.add) - 1 + red.exp[sing.start.idx:sing.stop.idx, ] <- final_response_matrix[singletons.to.add, ] + + #add the TFs to red.exp + tfs.start.idx = 1 + tfs.stop.idx = tfs.start.idx + length(tf.names) - 1 + red.exp[tfs.start.idx:tfs.stop.idx, ] <- final_response_matrix[tf.names,] + #create rownames: its either the bicluster, or the name of the TF, if the bicluster is a singleton then we use the name of the gene + if (is.null(names(cS))) { + r.names <- c(paste(sep="","BC_",seq(1:length(cS)))) + r.names <- c(tf.names, r.names, singletons.to.add)#r.names[ which(cs.num.rows == 1) ] <- singletons + } else { + r.names <- c(tf.names,names(cS), singletons.to.add) + } + rownames(red.exp) <- r.names + + #add the indexing vectors for the TFs and singletons, + #this is trivial as for the TFs they have expression in every condition + resp_idx[c(tfs.start.idx:tfs.stop.idx, sing.start.idx:sing.stop.idx) , ] <- t(matrix(rep(1:ncol(final_response_matrix),length(tf.names) + length(singletons.to.add)), + nrow = ncol(final_response_matrix), ncol= length(tf.names) + length(singletons.to.add))) + final_response_matrix <- red.exp + + #finishing the final_design_matrix + if(make.des.red.exp){ + #des.red.exp[(length(cS)+1):nrow(des.red.exp), ] <- final_design_matrix[tf.names,] + des.red.exp[sing.start.idx:sing.stop.idx, ] <- final_design_matrix[singletons.to.add, ] + #add the TFs to the design red.exp + des.red.exp[tfs.start.idx:tfs.stop.idx, ] <- final_design_matrix[tf.names, ] + + if (is.null(names(cS))) { + r.names <- c(paste(sep="","BC_",seq(1:length(cS)))) + r.names <- c(tf.names, r.names, singletons.to.add) + #r.names[ which(cs.num.rows == 1) ] <- singletons + } else { + r.names <- c(tf.names, names(cS), singletons.to.add) + } + rownames(des.red.exp) <- r.names + final_design_matrix <- des.red.exp + } else { + final_design_matrix <- final_design_matrix[tf.names, ] + } + }else{ + resp_idx <- t(matrix(rep(c(1:ncol(final_response_matrix)), nrow(final_response_matrix)), ncol = nrow(final_response_matrix), nrow=ncol(final_response_matrix))) + } + + return (list(final_response_matrix, final_design_matrix, resp_idx)) +} + + + +################################################################################ +# CH Jun 3, 2014 +# All the functions in this file should be replaced with something sane, but +# I don't have time for this right now. + +design.and.response.old <- function(meta.data, exp.mat, delT.min, delT.max, tau, + clusterStack, tf.names, time_delayed=T, + all_intervals=F, use_t0_as_steady_state=F, + use_delt_bigger_than_cutoff_as_steady_state=T) { + # get design matrix + x <- get_usr_chosen_design(meta.data, exp.mat, delT.min, + delT.max, time_delayed=T, all_intervals=F, + use_t0_as_steady_state=F, + use_delt_bigger_than_cutoff_as_steady_state=T) + design_matrix_steady_state <- x[[1]] + design_matrix_time_series <- x[[2]] + + # get response matrix + x <- get_usr_chosen_response(meta.data, exp.mat, delT.min, + delT.max, 'inf_1', tau, + use_t0_as_steady_state=F, + use_delt_bigger_than_cutoff_as_steady_state=T) + response_matrix_steady_state <- x[[1]] + response_matrix_time_series <- x[[2]] + + # make final design/response matrices + x <- make_final_design_and_response_matrix(design_matrix_steady_state, + design_matrix_time_series, + response_matrix_steady_state, + response_matrix_time_series, + 'all', clusterStack, exp.mat, + tf.names, make.des.red.exp=T) + + return(list(final_design_matrix=x[[2]], final_response_matrix=x[[1]], resp.idx=x[[3]])) +} + + +# CH Aug 15, 2014 +# This replaces all of the above code and should be more robust. + +# notes +# design matrix is same as exp.mat leaving out last time points +# response matrix is same as design for steady state; linear interpolation else +design.and.response <- function(meta.data, exp.mat, delT.min, delT.max, tau) { + + cond <- as.character(meta.data$condName) + prev <- as.character(meta.data$prevCol) + delt <- meta.data$del.t + + # break time series if del.t is larger than delT.max + prev[delt > delT.max] <- NA + delt[delt > delT.max] <- NA + + # fix condition names if needed (R is picky about row and column names and + # might have used different condition names in expression matrix) + not.in.mat <- setdiff(cond, colnames(exp.mat)) + cond.dup <- duplicated(cond) + if (length(not.in.mat) > 0) { + cond <- gsub('[/+-]', '.', cond) + prev <- gsub('[/+-]', '.', prev) + if (!all(cond.dup == duplicated(cond))) { + stop('Tried to fix condition names in meta data so that they would match column names in expression matrix, but failed') + } + } + # check if there are condition names missing in expression matrix + not.in.mat <- setdiff(cond, colnames(exp.mat)) + if (length(not.in.mat) > 0) { + print(not.in.mat) + stop('Error when creating design and response. The conditions printed above are in the meta data, but not in the expression matrix') + } + + des.mat <- matrix(0, nrow(exp.mat), 0, dimnames=list(rownames(exp.mat), NULL)) + res.mat <- matrix(0, nrow(exp.mat), 0, dimnames=list(rownames(exp.mat), NULL)) + + # handle all the steady state conditions first + steady <- is.na(prev) & !(cond %in% prev) + des.mat <- cbind(des.mat, exp.mat[, cond[steady]]) + res.mat <- cbind(res.mat, exp.mat[, cond[steady]]) + + # handle time series + for (i in which(!steady)) { + # find the conditions that follow this one and are at least delT.min far apart + following <- which(prev == cond[i]) + following.delt <- delt[following] + off <- which(following.delt < delT.min) + while (length(off) > 0) { + off.fol <- which(prev == cond[following[off[1]]]) + off.fol.delt <- delt[off.fol] + following <- c(following[-off[1]], off.fol) + following.delt <- c(following.delt[-off[1]], off.fol.delt + following.delt[off[1]]) + off <- which(following.delt < delT.min) + } + + # for each following condition (time point) + n <- length(following) + cntr <- 1 + for (j in following) { + if (n > 1) { + this.cond <- sprintf('%s_dupl%02d', cond[i], cntr) + } else { + this.cond <- cond[i] + } + des.mat <- cbind(des.mat, exp.mat[, cond[i]]) + colnames(des.mat)[ncol(des.mat)] <- this.cond + + interp.res <- tau / following.delt[cntr] * (exp.mat[, cond[j]] - exp.mat[, cond[i]]) + exp.mat[, cond[i]] + res.mat <- cbind(res.mat, interp.res) + colnames(res.mat)[ncol(res.mat)] <- this.cond + + cntr <- cntr + 1 + } + + # special case: nothing is following this condition within delT.min + # and it is the first of a time series --- treat as steady state + if (n == 0 & is.na(prev[i])) { + des.mat <- cbind(des.mat, exp.mat[, cond[i]]) + colnames(des.mat)[ncol(des.mat)] <- cond[i] + res.mat <- cbind(res.mat, exp.mat[, cond[i]]) + colnames(res.mat)[ncol(res.mat)] <- cond[i] + } + + } + + resp.idx <- t(matrix(1:ncol(res.mat), ncol(res.mat), nrow(exp.mat))) + return(list(final_design_matrix=des.mat, final_response_matrix=res.mat, resp.idx=resp.idx)) +} + diff --git a/Inferelator/R_scripts/dream8_out.R b/Inferelator/R_scripts/dream8_out.R new file mode 100644 index 0000000..6f3d4ed --- /dev/null +++ b/Inferelator/R_scripts/dream8_out.R @@ -0,0 +1,35 @@ + +dream8.out <- function(scores, cell.type, stimulus, + team.name='Netzwerk', + outdir='~/Projects/DREAM8/submission/') { + + sif.lines <- c() + eda.lines <- 'EdgeScore' + + for (i in 1:ncol(scores)) { + for (j in 1:nrow(scores)) { + if (scores[j, i] > 0) { + sif.lines <- c(sif.lines, paste(colnames(scores)[i], '1', rownames(scores)[j], sep='\t')) + eda.lines <- c(eda.lines, paste(colnames(scores)[i], ' (1) ', rownames(scores)[j], ' = ', scores[j, i], sep='')) + } + } + } + degree <- apply(scores, 1, sum) + degree[1:ncol(scores)] <- degree[1:ncol(scores)] + apply(scores, 2, sum) + for (i in 1:length(degree)) { + if (degree[i] == 0) { + sif.lines <- c(sif.lines, rownames(scores)[i]) + } + } + sif.lines <- sort(unique(sif.lines)) + + if (is.null(cell.type)) { + base.name <- paste(team.name, 'Network-Insilico', sep='-') + } else { + base.name <- paste(team.name, cell.type, stimulus, 'Network', sep='-') + } + + cat(sif.lines, file=paste(outdir, base.name, '.sif', sep=''), sep='\n') + cat(eda.lines, file=paste(outdir, base.name, '.eda', sep=''), sep='\n') + +} diff --git a/Inferelator/R_scripts/evaluate.R b/Inferelator/R_scripts/evaluate.R new file mode 100644 index 0000000..a00b589 --- /dev/null +++ b/Inferelator/R_scripts/evaluate.R @@ -0,0 +1,54 @@ +ChristophsPR <- function(ord.idx, gs) { + prec <- cumsum(gs[ord.idx]) / cumsum(rep(1, length(ord.idx))) + rec <- cumsum(gs[ord.idx]) / sum(gs) + + prec <- c(prec[1], prec) + rec <- c(0, rec) + + auc <- ChristophsAUC(rec, prec) + return(list(prec=prec, rec=rec, auc=auc)) +} + + +ChristophsAUC <- function(x, y) { + dx <- diff(x) + my <- y[1:(length(y) - 1)] + diff(y) / 2 + return(sum(dx * my)) +} + + +aupr <- function(mat, gs, eval.on.subset=FALSE) { + rows <- rownames(gs) + cols <- colnames(gs) + if (eval.on.subset) { + rows <- rownames(gs)[apply(gs != 0, 1, sum) > 0] + cols <- colnames(gs)[apply(gs != 0, 2, sum) > 0] + } + return(ChristophsPR(order(mat[rows, cols], decreasing=TRUE), gs[rows, cols] != 0)$auc) +} + + +summarizeResults <- function(full.dir, eval.on.subset) { + require('Matrix') + params.and.input <- paste(full.dir, 'params_and_input.RData', sep='/') + if (!file.exists(params.and.input)) { + cat('No params_and_input.RData - skipping', full.dir, '\n') + return() + } + load(params.and.input) + files <- list.files(full.dir, "combinedconf_.+\\.RData$") + print(file.path(full.dir, files)) + + gs <- IN$gs.mat + + out <- matrix('', 0, 2) + for (res.file in files) { + load(file.path(full.dir, res.file)) + aupr.tot <- aupr(comb.confs, gs, eval.on.subset) + out <- rbind(out, c(res.file, aupr.tot)) + } + + # write results for this directory + write.table(out, file=file.path(full.dir, 'auprs.tsv'), quote=FALSE, sep='\t', row.names=FALSE, col.names=FALSE) +} + diff --git a/Inferelator/R_scripts/group_predictors.R b/Inferelator/R_scripts/group_predictors.R new file mode 100644 index 0000000..3212eee --- /dev/null +++ b/Inferelator/R_scripts/group_predictors.R @@ -0,0 +1,121 @@ + +pretty.much.the.same <- function(x, tol=.Machine$double.eps) { + return(all(abs(x - x[1]) < tol)) +} + +# get all nodes that can be reached from cur.node +get.cc <- function(cur.node, adj.mat, visited=c()) { + visited <- union(visited, cur.node) + new.adj.nodes <- setdiff(which(adj.mat[cur.node, ] > 0), visited) + for (a.node in new.adj.nodes) { + visited <- union(visited, get.cc(a.node, adj.mat, visited)) + } + return(visited) +} + +# get all connected components (with size > 1) +get.all.cc <- function(adj.mat) { + N <- nrow(adj.mat) + in.list <- rep(FALSE, N) + cc.list <- list() + for (i in 1:N) { + if (!in.list[i]) { + cc <- get.cc(i, adj.mat) + if (length(cc) > 1) { + cc.list[[length(cc.list) + 1]] <- cc + } + in.list[cc] <- TRUE + } + } + return(cc.list) +} + +group.prior <- function(groups, prior.mat, tfs.keep) { + if (is.null(prior.mat)) { + return(prior.mat) + } + prior.mat.grp <- matrix(0, nrow(prior.mat), length(groups)) + colnames(prior.mat.grp) <- names(groups) + g.n <- 1 + for (group in groups) { + #when consolidating the prior, check for inconsistencies + tmp.p.mat <- prior.mat[, group] + tmp.p.mat.incons <- any(apply(tmp.p.mat, 1, function(x) any(x > 0) & any(x < 0))) + if (tmp.p.mat.incons) { + stop(sprintf('inconsistency when consolidating the prior/GS in group.predictors for group %d', g.n)) + } + prior.mat.grp[, g.n] <- sign(apply(tmp.p.mat, 1, sum)) + g.n <- g.n + 1 + } + new.prior.mat <- prior.mat[, sort(setdiff(tfs.keep, unlist(groups)))] + new.prior.mat <- cbind(new.prior.mat, prior.mat.grp) + return(new.prior.mat) +} + +group.expression <- function(groups, exp.mat) { + grp.exp.mat <- matrix(0, length(groups), ncol(exp.mat), + dimnames=list(names(groups), colnames(exp.mat))) + for (group in names(groups)) { + genes <- intersect(groups[[group]], rownames(exp.mat)) + grp.exp.mat[group, ] <- apply(scale(t(exp.mat[genes, , drop=FALSE])), 1, mean) + } + return(grp.exp.mat) +} + +# des.mat is TFs x conditions matrix +# bs.pi is bs x conditions matrix of indices of conditions per bootstrap +group.predictors <- function(des.mat, prior.mat, gs.mat, bs.pi, cor.th=0.99, + grp.pre='pred.group.') { + tfs <- rownames(des.mat) + have.na <- apply(is.na(des.mat), 1, sum) > 0 + const <- rep(FALSE, nrow(des.mat)) + const[!have.na] <- apply(des.mat[!have.na, ], 1, pretty.much.the.same) + + # of the TFs that have no NA and are not constant, which ones are highly correlated + # in any of the sub-samples of the data + keep <- !have.na & !const + tfs.keep <- tfs[keep] + cor.mat <- matrix(0, sum(keep), sum(keep)) + for (i in 1:nrow(bs.pi)) { + bs.cor.mat <- cor(t(des.mat[keep, bs.pi[i, ]])) + cor.mat <- cor.mat + (bs.cor.mat > cor.th) + } + cc <- get.all.cc(cor.mat) + groups <- lapply(cc, function(x) tfs.keep[x]) + + if (length(groups) > 0) { + names(groups) <- sprintf('%s%d', grp.pre, 1:length(groups)) + } + + # apply the grouping to design matrix, exclude NA containing and constant predictors + des.mat.grp <- matrix(0, length(groups), ncol(des.mat)) + rownames(des.mat.grp) <- names(groups) + g.n <- 1 + for (group in groups) { + exemplar <- group[which.max(apply(des.mat[group, ], 1, function(x) sd(x)/mean(x)))] + des.mat.grp[g.n, ] <- des.mat[exemplar, ] + g.n <- g.n + 1 + } + new.des.mat <- des.mat[sort(setdiff(tfs.keep, unlist(groups))), ] + new.des.mat <- rbind(new.des.mat, des.mat.grp) + + # also adjust the prior matrix and gold standard to the grouped predictors + new.prior.mat <- group.prior(groups, prior.mat, tfs.keep) + new.gs.mat <- group.prior(groups, gs.mat, tfs.keep) + + + # if no TF was excluded and no TFs were grouped, return original design matrix + # and original prior matrix + if (identical(sort(rownames(des.mat)), sort(rownames(new.des.mat)))) { + new.des.mat <- des.mat + new.prior.mat <- prior.mat + new.gs.mat <- gs.mat + } + + return(list(pred.has.na=tfs[have.na], pred.is.const=tfs[const], + pred.groups=groups, des.mat=new.des.mat, prior.mat=new.prior.mat, + gs.mat=new.gs.mat, tf.names=rownames(new.des.mat))) +} + + + diff --git a/Inferelator/R_scripts/men.R b/Inferelator/R_scripts/men.R new file mode 100644 index 0000000..a7041d8 --- /dev/null +++ b/Inferelator/R_scripts/men.R @@ -0,0 +1,555 @@ +callMEN <- function(ind, Xs, Y, clr.mat, nS, + nCv = 10,lambda = 0, + sparseModels=T, + verbose = FALSE, + plot.it = FALSE, + plot.file.name = NULL, + weights.mat=weights.mat, + no.pr.val = no.pr.val, + use_interactions = FALSE){ + + require('elasticnet') + + in.weights = weights.mat[ind, ] + + #since we do cross-validation, we + #need to get only the unique condtions + keep <- (1:ncol(Y))[!duplicated(colnames(Y))] + + Y_ind <- Y[ind, keep] + + Xs <- Xs[, keep] + + #setting which predictors we consider + + pp <- rep(F, nrow(Xs)) + clr.row <- clr.mat[ind, ] + + # send self predictor to end of list if gene we are inferring on is a TF + # also remove as prior if present + if (ind <= nrow(Xs)) { + clr.row[ind] <- -Inf + in.weights[ind] <- no.pr.val + } + + clr.order <- order(clr.row, decreasing=TRUE) + have.priors <- which(in.weights != no.pr.val) + + # potential predictors are the union of predictors with priors and the top + # nS ones based on clr matrix + pp[unique(c(have.priors, clr.order[1:nS]))] <- TRUE + + #creating the design matrix + Xs.priors <- Xs[pp, ,drop=F] + rownames(Xs.priors) <- which(pp == TRUE) + + #getting the prior weights for the elements of the design matrix + in.weights.vec <- in.weights[pp] + names(in.weights.vec) <- rownames(Xs.priors) + + if(plot.it){ + plot.new() + title(paste("CV curves for target gene ", ind, sep = "")) + } + + #calculating the MEN models for all chosen values + #of lambda + if (verbose) + cat("Model for target ", ind, "\n") + + adaEnet.res <- cvAdaptiveEnetAll(Xs.priors, Y_ind, k.folds = nCv, lambda.vec = lambda, + s.fraction = c( seq(from = 0, to = 0.1, length = 100), seq(from = 0.11, to = 1, length = 90)), + weights.type="other", in.weights.vec = in.weights.vec, trace = FALSE, + plot.it = plot.it, se = TRUE)#, sweep.out=sweep.out, run.van = run.van)#, no.pr.val = no.pr.val) + + + #finding the parameters which minimize the cross-validation (cv) error + which.err <- "error.cv.min" #minimum cv error + which.coefs <- "coefs.cv.min" #coefficients at the minimum cv error + if (sparseModels) { + #error and coefs one standard deviation to the left + #of min cv error (results in sparser model) + which.err <- "error.sparse" + which.coefs <- "coefs.sparse" + } + + models.errors <- unlist(lapply(adaEnet.res, function(i) i[[which.err]])) + min.model <- which.min(models.errors) + + models.coefs <- adaEnet.res[[min.model]][[which.coefs]] + not.zero <- models.coefs != 0 + + if (verbose) { + cat("\nCV-min errors are: ", sapply(adaEnet.res, function(i) i$"error.cv.min"), "\n") + cat("Sparse errors are: ", sapply(adaEnet.res, function(i) i$"error.sparse"), "\n\n") + } + + #creating matrix to store the output + beta.mat = matrix(0,ncol=6,nrow=0) + colnames(beta.mat) = c("trgt","tf","beta","prd_xpln_var","min.l2","bias") + + refit.model <- NULL + if (!any(not.zero)) { #if we have a null model, we return the empty beta.mat + return(list(ind=ind, pp=TRUE, betas=0, betas.resc=0)) + obj <- list() + obj[["priors"]] <- list(ind = ind, bM = beta.mat, allLambdaRes = adaEnet.res) + return(obj) + } + + #reffiting betas + x <- refitModels(Y_ind, t(Xs.priors[not.zero, ,drop = F]), models.coefs[not.zero], center = F) + betas.ref <- x$betas + bias <- x$bias + rm(x) + + #rescaling betas + betas.resc <- rescaleModels(Y_ind, t(Xs.priors[names(betas.ref), ,drop = F]), betas.ref, center = F, use.ev = F) + + for (i in 1:length(betas.resc)) + beta.mat <- rbind(beta.mat, c(ind, as.numeric(names(betas.ref)[i]), as.numeric(betas.ref[names(betas.resc)[i]]), + betas.resc[i], as.numeric(names(models.errors)[min.model]), bias)) + + return(list(ind=ind, pp=beta.mat[, 'tf'], betas=beta.mat[, 'beta'], betas.resc=beta.mat[, 'prd_xpln_var'])) + + obj <- list() + obj[["priors"]] <- list(ind = ind, bM = beta.mat, allLambdaRes = adaEnet.res) + return(obj) +} + + +refitModels <- function(y, x, in.coefs, center = F) { + if (center == T) { + #cat("centering predictors and response prior to refitting \n") + n <- dim(x)[1] + one <- rep(1, n) + meanx <- drop(one %*% x)/n + x <- scale(x, meanx, FALSE) + normx <- sqrt(drop(one %*% (x^2))) + x <- scale(x, FALSE, normx) + + #scale Y by its mean + y <- drop(y - mean(y)) + } + + refit.model <- lm(y ~ x) + refit.coefs <- refit.model$coefficients[-1] + names(refit.coefs) <- names(in.coefs) + bias <- refit.model$coefficients[1] + + return(list(betas = refit.coefs, bias = bias)) +} + +rescaleModels <- function(y, x, model.coefs, center = F, use.ev = F) { + #not.zero <- model.coefs != 0 + + if (center) { + #cat("centering predictors and response prior to refitting \n") + n <- dim(x)[1] + one <- rep(1, n) + meanx <- drop(one %*% x)/n + x <- scale(x, meanx, FALSE) + normx <- sqrt(drop(one %*% (x^2))) + x <- scale(x, FALSE, normx) + + #scale Y by its mean + y <- drop(y - mean(y)) + } + + refit.func <- refitBySSE + if (use.ev) + refit.func <- PredErrRed + + betas.resc <- refit.func(y, x, model.coefs) + names(betas.resc) <- names(model.coefs)#[which(not.zero)] + + return(betas.resc) +} + +refitBySSE <- function(Y, X, orig.coefs){ + if (!any(orig.coefs != 0)) + return(rep(0, length(orig.coefs))) + + non.zero.preds <- names(which(orig.coefs != 0)) + lin.model <- lm(Y ~ X[, non.zero.preds, drop=FALSE]) + + coefs <- lin.model$coefficients[-1] + bias <- lin.model$coefficients[1] + names(coefs) <- names(which(orig.coefs != 0)) + + resids <- lin.model$residuals + #modVals <- bias + X[,non.zero.preds,drop=F]%*%as.matrix(coefs) #calculating the resultant model + #----------how much error is explained by the model-------------# + YHat.SSE <- sum(resids^2) + y.SSE <- sum((Y-mean(Y))^2)#sum(Y^2) + rel.model.err <- YHat.SSE/y.SSE #this measures how much better the model is than the + #the null model + #----------done calculating the amount of error explained by the model-------------------# + names(coefs) <- non.zero.preds + + #-----------calculate contribution of each beta-----------------------# + #---this is done by dividing the residual of the full model-----------# + #---by the residual of the model without a particular beta------------# + beta.contrib <- vector("numeric",length(coefs)) + names(beta.contrib) <- non.zero.preds + + for(m.comp in names(beta.contrib)){ + in.model <- non.zero.preds[-which(non.zero.preds == m.comp)] + if(length(beta.contrib) == 1){ + cur.y.hat <- bias + }else{ + cur.y.hat <- bias*rep(1,nrow(X)) + X[,in.model,drop=F]%*%as.matrix(coefs[in.model]) + } + cur.y.hat.sse <- sum((Y - cur.y.hat)^2) + beta.contrib[m.comp] = 1 - (YHat.SSE/cur.y.hat.sse) + } + + object <- list() + object[["coefs"]] <- coefs + object[["bias"]] <- bias + object[["resids"]] <- resids + object[["relError"]] <- rel.model.err + object[["SSR"]] <- YHat.SSE + object[["SST"]] <- y.SSE + object[["beta.contrib"]] <- beta.contrib + + #return(object) + return(beta.contrib) +} + + +#wrapper function which runs cvAdaptiveEnet vanilla version, with sweep.out, and with priors +#assumes priors and sweep.out are both not null +cvAdaptiveEnetAll <- function(x, y, k.folds = 10, in.folds = NULL, lambda.vec = c(0), nCv=10, s.fraction = seq(from = 0, to = 1, length = 100), weights.type = "other", + in.weights.vec = NULL,trace = FALSE, plot.it = FALSE, se = TRUE) {#, no.pr.val = 1){ + + #create one CV split to use for running elastic net + if( is.null(in.folds)){ + all.folds <- split(sample(1:length(y)), rep(1:k.folds, length = length(y)))#cv.folds(length(y), k.folds) + }else{ + all.folds = in.folds + } + x.priors <- cvAdaptiveEnet(t(x), y, k.folds = k.folds, lambda.vec = lambda.vec, s.fraction = s.fraction,# no.pr.val = no.pr.val, + weights.type = weights.type, in.folds=all.folds,in.weights.vec = in.weights.vec, trace = FALSE, plot.it = plot.it, se = TRUE) + + return(x.priors) +} + + +cvAdaptiveEnet <- function(x, y, k.folds = 10, lambda.vec = c(0), + s.fraction = seq(from = 0, to = 1, length = 100), weights.type = "other", in.folds=NULL, + in.weights.vec = NULL,trace = FALSE, plot.it = TRUE, se = TRUE) { + + all.preds.names <- colnames(x) + if( is.null(in.weights.vec) || (length(in.weights.vec) != ncol(x)) ){ + in.weights.vec <- rep(1,length=ncol(x)) + } + names(in.weights.vec) <- all.preds.names + + if(is.null(in.folds)){ + all.folds <- split(sample(1:length(y)), rep(1:k.folds, length = length(y)))#cv.folds(length(y), k.folds) + }else{ + all.folds <- in.folds + } + + all.lambda.obj <- list() + for(lambda in lambda.vec){ + cat(".") + residmat <- matrix(0, length(s.fraction), k.folds) + for (i in seq(k.folds)) { + omit <- all.folds[[i]] + + y.train <- y[-omit] + y.test <- y[omit] + + x.train <- x[-omit,,drop=F] + x.test <- x[omit,,drop=F] + + weights.vec <- NULL + if((weights.type == "other") && is.null(weights.vec)){ + if(any(in.weights.vec == 0)) + in.weights.vec[which(in.weights.vec == 0)] <- 1# no.pr.val + + weights.vec <- in.weights.vec + } + + fit <- enetWeights(x.train, y.train, trace = trace, lambda=lambda,in.weights=weights.vec) + fit <- predict(fit, x.test, mode = "fraction", s = s.fraction)$fit + + if (length(omit) == 1) + fit <- matrix(fit, nrow = 1) + + residmat[, i] <- apply((y.test - fit)^2, 2, mean) + if (trace) + cat("\n CV Fold", i, "\n\n") + } + + cv <- apply(residmat, 1, mean) + cv.error <- sqrt(apply(residmat, 1, var)/k.folds) + cv.res <- list(fraction = s.fraction, cv = cv, cv.error = cv.error) + cv.res[["mode"]] = "fraction" + cv.res[["index"]] = s.fraction + if (plot.it) #{ + plotCVLars(cv.res, se = se) + + enet.model <- enetWeights(x,y,trace = trace,lambda=lambda,in.weights=weights.vec) + + #now get the coefficients of the chosen model, and rescale, + #as described in adaptive-lars paper + + #getting coefs at cv.min + min.idx <- which.min(cv) + cv.opt.frac <- s.fraction[min.idx] + coefs.cv.min <- predict(enet.model, s=cv.opt.frac, type="coef", mode="fraction")$coefficients + + #gettings coefs at one st.dev away from cv.min + sparse.idx <- pickSparseModel(cv.res, plot.it, lambda) + cv.sparse.frac <- s.fraction[sparse.idx] + coefs.sparse <- predict(enet.model, s=cv.sparse.frac, type="coef", mode="fraction")$coefficients + + object <- list() + object[["cv.obj"]] <- cv.res + object[["coefs.cv.min"]] <- coefs.cv.min + object[["error.cv.min.idx"]] <- min.idx + object[["error.cv.min"]] <- cv[min.idx]/cv[1] #get relative cv error + object[["coefs.sparse"]] <- coefs.sparse + object[["error.sparse.idx"]] <- sparse.idx + object[["error.sparse"]] <- cv[sparse.idx]/cv[1] #get relative cv error + object[["weights"]] <- weights.vec + + all.lambda.obj[[paste(lambda,sep="")]] <- object + } + invisible(all.lambda.obj) +} + +enetWeights <- function (x, y, lambda = 0, max.steps, normalize = FALSE, intercept = TRUE, + trace = FALSE, eps = .Machine$double.eps, in.weights=NULL) +{ + call <- match.call() + nm <- dim(x) + n <- nm[1] + m <- nm[2] + im <- seq(m) + one <- rep(1, n) + vn <- dimnames(x)[[2]] + meanx <- drop(one %*% x)/n + if (intercept == FALSE) { + meanx <- rep(0, m) + } + x <- scale(x, meanx, FALSE) + normx <- sqrt(drop(one %*% (x^2))) + if (normalize == FALSE) { + normx <- rep(1, m) + } + if (any(normx < eps * sqrt(n))) + stop("Some of the columns of x have zero variance") + names(normx) <- NULL + x <- scale(x, FALSE, normx) + mu <- mean(y) + if (intercept == FALSE) { + mu <- 0 + } + y <- drop(y - mu) + + #adding scaling of predictors, x + #by in.weights here! + if(!is.null(in.weights)){ + x <- scale(x,FALSE,in.weights) + } + + d1 <- sqrt(lambda) + d2 <- 1/sqrt(1 + lambda) + Cvec <- drop(t(y) %*% x) * d2 + ssy <- sum(y^2) + residuals <- c(y, rep(0, m)) + if (lambda > 0) { + maxvars <- m + } + if (lambda == 0) { + maxvars <- min(m, n - 1) + } + if (missing(max.steps)) { + max.steps <- 50 * maxvars + } + L1norm <- 0 + penalty <- max(abs(Cvec)) + beta <- rep(0, m) + betactive <- list(NULL) + first.in <- integer(m) + active <- NULL + Actset <- list(NULL) + df <- 0 + if (lambda != 0) { + Cp <- ssy + } + ignores <- NULL + actions <- as.list(seq(max.steps)) + drops <- FALSE + Sign <- NULL + R <- NULL + k <- 0 + while ((k < max.steps) & (length(active) < maxvars)) { + action <- NULL + k <- k + 1 + inactive <- if (k == 1) + im + else im[-c(active, ignores)] + C <- Cvec[inactive] + Cmax <- max(abs(C)) + if (!any(drops)) { + new <- abs(C) == Cmax + C <- C[!new] + new <- inactive[new] + for (inew in new) { + R <- updateRR(x[, inew], R, x[, active], lambda) + if (attr(R, "rank") == length(active)) { + nR <- seq(length(active)) + R <- R[nR, nR, drop = FALSE] + attr(R, "rank") <- length(active) + ignores <- c(ignores, inew) + action <- c(action, -inew) + if (trace) + cat("LARS-EN Step", k, ":\t Variable", inew, + "\tcollinear; dropped for good\n") + } + else { + if (first.in[inew] == 0) + first.in[inew] <- k + active <- c(active, inew) + Sign <- c(Sign, sign(Cvec[inew])) + action <- c(action, inew) + if (trace) + cat("LARS-EN Step", k, ":\t Variable", inew, + "\tadded\n") + } + } + } + else action <- -dropid + Gi1 <- backsolve(R, backsolvet(R, Sign)) + A <- 1/sqrt(sum(Gi1 * Sign)) + w <- A * Gi1 + u1 <- drop(x[, active, drop = FALSE] %*% w * d2) + u2 <- rep(0, m) + u2[active] <- d1 * d2 * w + u <- c(u1, u2) + if (lambda > 0) { + maxvars <- m - length(ignores) + } + if (lambda == 0) { + maxvars <- min(m - length(ignores), n - 1) + } + if (length(active) >= maxvars) { + gamhat <- Cmax/A + } + else { + a <- (drop(u1 %*% x[, -c(active, ignores)]) + d1 * + u2[-c(active, ignores)]) * d2 + gam <- c((Cmax - C)/(A - a), (Cmax + C)/(A + a)) + gamhat <- min(gam[gam > eps], Cmax/A) + Cdrop <- c(C - gamhat * a, -C + gamhat * a) - (Cmax - + gamhat * A) + } + dropid <- NULL + b1 <- beta[active] + z1 <- -b1/w + zmin <- min(z1[z1 > eps], gamhat) + if (zmin < gamhat) { + gamhat <- zmin + drops <- z1 == zmin + } + else drops <- FALSE + beta[active] <- beta[active] + gamhat * w + betactive[[k]] <- beta[active] + Actset[[k]] <- active + residuals <- residuals - (gamhat * u) + Cvec <- (drop(t(residuals[1:n]) %*% x) + d1 * residuals[-(1:n)]) * + d2 + L1norm <- c(L1norm, sum(abs(beta[active]))/d2) + penalty <- c(penalty, penalty[k] - abs(gamhat * A)) + if (any(drops)) { + dropid <- seq(drops)[drops] + for (id in rev(dropid)) { + if (trace) + cat("LARS-EN Step", k, ":\t Variable", active[id], + "\tdropped\n") + R <- downdateR(R, id) + } + dropid <- active[drops] + beta[dropid] <- 0 + active <- active[!drops] + Sign <- Sign[!drops] + } + if (!is.null(vn)) + names(action) <- vn[abs(action)] + actions[[k]] <- action + } + allset <- Actset[[1]] + for (i in 2:k) { + allset <- union(allset, Actset[[i]]) + } + allset <- sort(allset) + max.p <- length(allset) + beta.pure <- matrix(0, k + 1, max.p) + for (i in 2:(k + 1)) { + for (j in 1:length(Actset[[i - 1]])) { + l <- c(1:max.p)[allset == Actset[[i - 1]][j]] + beta.pure[i, l] <- betactive[[i - 1]][j] + } + } + beta.pure <- beta.pure/d2 + dimnames(beta.pure) <- list(paste(0:k), vn[allset]) + k <- dim(beta.pure)[1] + df <- 1:k + for (i in 1:k) { + a <- drop(beta.pure[i, ]) + df[i] <- 1 + length(a[a != 0]) + } + residuals <- y - x[, allset, drop = FALSE] %*% t(beta.pure) + beta.pure <- scale(beta.pure, FALSE, normx[allset]) + RSS <- apply(residuals^2, 2, sum) + R2 <- 1 - RSS/RSS[1] + Cp <- ((n - m - 1) * RSS)/rev(RSS)[1] - n + 2 * df + object <- list(call = call, actions = actions[seq(k)], allset = allset, + beta.pure = beta.pure, vn = vn, mu = mu, normx = normx[allset], + meanx = meanx[allset], lambda = lambda, L1norm = L1norm, + penalty = penalty * 2/d2, df = df, Cp = Cp, sigma2 = rev(RSS)[1]/(n - + m - 1)) + class(object) <- "enet" + object +} + + +pickSparseModel <- function(cv.res, plot.it, lambda){ + cv <- cv.res$cv + s.fraction <- cv.res$fraction + cv.error <- cv.res$cv.error + + cv.min.idx <- which.min(cv) + best.cvErr.min <- Inf + thresh.cv <- cv[cv.min.idx] + cv.error[cv.min.idx]#/cv[1] #taking error one std dev from min + best.s <- 1 + + for (i in 1:cv.min.idx) { + if (cv[i] <= thresh.cv) { + best.s <- i + break + } + } + + if(plot.it){ + bestSRng <- range(cv[best.s]+.2*cv[best.s], cv[best.s]-.2*cv[best.s]) + lines(c(s.fraction[best.s],s.fraction[best.s]), range(cv[best.s]+.02*cv[best.s], cv[best.s]-.02*cv[best.s]), col=colors()[258], lty=1, lwd=3) + + errRng <- range(cv[cv.min.idx] + cv.error[cv.min.idx], cv[cv.min.idx] - cv.error[cv.min.idx]) + lines(c(s.fraction[cv.min.idx],s.fraction[cv.min.idx]), errRng, col=2, lty=2, lwd=3) # red line denoting where the minimum is + lines(range(s.fraction), c(thresh.cv,thresh.cv), col=4, lty=2, lwd=3) #blue line going to where one std. dev. above the min intersects the curve + #title(paste("enet curve for biclust: ",ind," w/ l2 weight: ",l2)) + + + mtext(paste("l2 weight of: ", lambda, sep = ""), 3, line = -1) + } + #min.idx <- best.s + sparse.idx = best.s + + return(sparse.idx) +} + diff --git a/Inferelator/R_scripts/mi_and_clr.R b/Inferelator/R_scripts/mi_and_clr.R new file mode 100644 index 0000000..67f1d6d --- /dev/null +++ b/Inferelator/R_scripts/mi_and_clr.R @@ -0,0 +1,122 @@ +library("inline") +if ('parallel' %in% installed.packages()[, 'Package']) { + library('parallel') +} else { + library('multicore') +} + +discretize <- function(X, nbins) { + N <- length(X) + X.min <- min(X) + X.max <- max(X) + tiny <- max(.Machine$double.eps * (X.max - X.min), .Machine$double.eps) + X.disc <- floor((X - X.min) / (X.max - X.min + tiny) * nbins) + return(as.integer(X.disc)) +} + + +# compute MI of each colum in x to each column in y +# you'll want the larger matrix to be x +mi <- function(x, y, nbins=10, cpu.n=1, perm.mat=NULL) { + if(!is.null(perm.mat)) { + for (i in 1:ncol(x)) { + x[, i] <- x[perm.mat[, i], i] + } + for (i in 1:ncol(y)) { + y[, i] <- y[perm.mat[, i], i] + } + } + + # discretize the columns + x <- apply(x, 2, discretize, nbins) + y <- apply(y, 2, discretize, nbins) + + m <- ncol(x) + n <- ncol(y) + ret <- matrix(0, m, n, dimnames=list(colnames(x), colnames(y))) + + if (cpu.n == 1) { + s <- nrow(x) + return(matrix(mi.in.c4(s, nbins, x, m, y, n, ret)$mi, m, dimnames=list(colnames(x), colnames(y)))) + } + + col.list <- split(1:m, cut(1:m, cpu.n, labels=F)) + ret.list <- mclapply(col.list, mi.thread, x, y, nbins, mc.cores = cpu.n) + for (i in 1:length(ret.list)) { + ret[col.list[[i]], ] <- ret.list[[i]] + } + return(ret) +} + + +mi.thread <- function(cols, x, y, nbins) { + x <- x[, cols, drop = FALSE] + m <- ncol(x) + n <- ncol(y) + s <- nrow(x) + ret <- matrix(0, m, n) + return(matrix(mi.in.c4(s, nbins, x, m, y, n, ret)$mi, m)) +} + +mi.in.c4.sig <- signature(n = 'integer', k = 'integer', x = 'integer', + ncolx = 'integer', y = 'integer', + ncoly = 'integer', mi = 'numeric') +mi.in.c4.code <- ' + int i, j, cx, cy; + double *pxy, *px, *py; + + py = (double *)calloc(*k * *ncoly, sizeof(double)); + for (cy = 0; cy < *ncoly; ++cy) { + for (i = 0; i < *n; ++i) { + ++py[cy * *k + y[cy * *n + i]]; + } + } + + for (cx = 0; cx < *ncolx; ++cx) { + for (cy = 0; cy < *ncoly; ++cy) { + pxy = (double *)calloc(*k * *k, sizeof(double)); + px = (double *)calloc(*k, sizeof(double)); + + for (i = 0; i < *n; ++i) { + ++pxy[(y[cy * *n + i]) * (*k) + (x[cx * *n + i])]; + ++px[x[cx * *n + i]]; + } + for (i = 0; i < *k; ++i) { + for (j = 0; j < *k; ++j) { + if (pxy[j * (*k) + i] > 0) { + mi[cy * *ncolx + cx] += pxy[j * (*k) + i] / *n * + log(pxy[j * (*k) + i] / *n / + (px[i] / *n * py[cy * *k + j] / *n)); + } + } + } + free(px); + free(pxy); + } + } + free(py);' +mi.in.c4 <- cfunction(mi.in.c4.sig, mi.in.c4.code, convention=".C") + + +toZscore <- function(x, bg = NULL) { + if (is.null(bg) == T) { + return((x - mean(x, na.rm = T)) / sd(x, na.rm = T)) + } + return((x - mean(bg, na.rm = T)) / sd(bg, na.rm = T)) +} + + +mixedCLR <- function(mi.stat, mi.dyn) { + z.r.dyn <- t(apply(mi.dyn, 1, toZscore)) + z.r.dyn[z.r.dyn < 0] <- 0 + + z.c.mix <- mi.dyn + for (j in 1:ncol(mi.stat)) { + z.c.mix[, j] <- (z.c.mix[, j] - mean(mi.stat[, j], na.rm = T)) / sd(mi.stat[, j], na.rm = T) + } + z.c.mix[z.c.mix < 0] <- 0 + + mclr <- sqrt(z.r.dyn ^ 2 + z.c.mix ^ 2) + mclr[is.nan(mclr)] <- NA + return(mclr) +} diff --git a/Inferelator/R_scripts/net_report.Rmd b/Inferelator/R_scripts/net_report.Rmd new file mode 100644 index 0000000..69ee150 --- /dev/null +++ b/Inferelator/R_scripts/net_report.Rmd @@ -0,0 +1,240 @@ + +# This is a network report generated on `r format(Sys.time(), "%Y-%m-%d %H-%M-%S")` +# by `r Sys.info()['login']` with `r version$version.string` +# on a `r version$platform` platform. + +This report is based on the file `r basename(cc.file)` in directory **`r dirname(cc.file)`**. + + + +```{r, include=FALSE} +library('Matrix') + +in.dir <- dirname(cc.file) + +load(cc.file) +load(gsub('combinedconf', 'betas', cc.file)) +load(paste(in.dir, 'params_and_input.RData', sep='/')) + +if (is.null(colnames(comb.confs))) { + colnames(comb.confs) <- IN$tf.names + rownames(comb.confs) <- rownames(IN$exp.mat) +} + +beta.sign <- as.matrix(betas[[1]] * 0) +for (n in 1:length(betas)) { + beta.sign <- beta.sign + sign(betas[[n]]) +} +dimnames(beta.sign) <- dimnames(comb.confs) + +# match the ccfile to the prior that was used + p.ind <- which(sapply(names(IN$priors), function(n) grepl(n, cc.file))) + p.name <- names(IN$priors)[p.ind] + p.mat <- IN$grouped.pred[[p.name]]$prior.mat[rownames(beta.sign), colnames(beta.sign)] + +# get median rescaled beta +resc.betas <- lapply(betas.resc, function(x) x[beta.sign != 0]) +resc.betas <- matrix(unlist(resc.betas), length(resc.betas[[1]])) +resc.betas.median <- apply(resc.betas, 1, median) + +# create data frame of non-zero scores +non.zero <- which(beta.sign != 0, arr.ind=TRUE) +vec.ind <- which(beta.sign != 0) +net.df <- data.frame(regulator=colnames(beta.sign)[non.zero[, 2]], + target=rownames(beta.sign)[non.zero[, 1]], + beta.sign=beta.sign[vec.ind], + var.exp.median=resc.betas.median, + prior=p.mat[vec.ind], + vec.ind=vec.ind) + +var.exp <- as.matrix(betas.resc[[1]] * 0) +var.exp[net.df$vec.ind] <- net.df$var.exp.median + +VAR.EXP.TH <- 0.2 +BETA.SIGN.TH <- 0.5 * PARS$num.boots + + +net.stats <- function(prior.mat) { + cat('prior.mat.stats\n') + cat('non-zero entries:\t') + edges <- sum(prior.mat != 0) + cat(edges, '\n') + cat('TFs with targets:\t') + tfs <- sum(apply(prior.mat != 0, 2, sum) > 0) + cat(tfs, '\n') + cat('genes with regulators:\t') + targets <- sum(apply(prior.mat != 0, 1, sum) > 0) + cat(targets, '\n') + cat('average out degree:\t', edges / tfs, '\n') + cat('average in degree:\t', edges / targets, '\n') +} +``` + +Time used for this network inference run (in hours): +```{r, echo=FALSE} +print(round(PROCTIME/3600, 2)) +``` + +# Inferred net based on beta sign threshold + +Table of the sign of interactions added across all `r PARS$num.boots` bootstraps. +```{r} +bsv <- as.vector(beta.sign) + +table(bsv) +plot(table(bsv[abs(bsv) >= BETA.SIGN.TH])) + +# total nuber of interactions +sum(abs(bsv) >= BETA.SIGN.TH) + +# table of out-degree and counts +out.tab <- table(subset(net.df, abs(beta.sign) >= BETA.SIGN.TH)$regulator) +table(out.tab) +# show how many regulators (TFs) have targets +table(out.tab > 0) + +# print the regulators with the most targets +head(sort(out.tab, decreasing=TRUE)) + +# table of in-degree and counts +in.tab <- table(subset(net.df, abs(beta.sign) >= BETA.SIGN.TH)$target) +table(in.tab) +# show how many targets have at least one regulator +table(in.tab > 0) + +# print the targets with most regulators +head(sort(in.tab, decreasing=TRUE)) + +# dimensionality of input expression +dim(IN$exp.mat) +``` + + +# Inferred net based on explained variance threshold +```{r} +# how does the median explained variance compare to the beta sign +require('ggplot2') +g <- ggplot(net.df, aes(x=factor(beta.sign), y=var.exp.median)) + geom_boxplot() + geom_hline(yintercept=VAR.EXP.TH, color='red') +plot(g) + +ia.order <- order(net.df$var.exp.median, decreasing=TRUE) +tmp <- subset(net.df[ia.order, ], var.exp.median >= 0.1) +plot(tmp$var.exp.median, 1:nrow(tmp), 'l', xlab='variance explained threshold', ylab='number of interactions in network') +abline(v=VAR.EXP.TH, col='red') + +vev <- as.vector(net.df$var.exp.median) +table(cut(vev, 20)) + +vev.b <- cut(vev, 20) +.pardefault <- par(no.readonly = T) +par(mar=c(8,4,2,1)) +plot(as.vector(table(vev.b, exclude=levels(vev.b)[1])), pch=19, xaxt='n', xlab='', ylab='counts', main='explained variance') +axis(1, at=1:(length(levels(vev.b))-1), labels=levels(vev.b)[-1], las=2) +par(.pardefault) + + + +# total nuber of interactions +sum(vev >= VAR.EXP.TH) + +# table of out-degree and counts +out.tab <- table(subset(net.df, var.exp.median >= VAR.EXP.TH)$regulator) +table(out.tab) +# show how many regulators (TFs) have targets +table(out.tab > 0) + +# print the regulators with the most targets +head(sort(out.tab, decreasing=TRUE)) + +# table of in-degree and counts +in.tab <- table(subset(net.df, var.exp.median >= VAR.EXP.TH)$target) +table(in.tab) +# show how many targets have at least one regulator +table(in.tab > 0) + +# print the targets with most regulators +head(sort(in.tab, decreasing=TRUE)) + + +``` + +# Re: grouped predictors +In this inference run, `r length(IN$priors)` sets of priors were used. +These are the names: +```{r, echo=FALSE} +names(IN$priors) +``` +These are the details regarding the grouping of predictors given the prior `r p.name`. +```{r, echo=FALSE} +gp <- IN$grouped.pred[[p.name]] +``` +A total of `r length(gp$pred.has.na)` predictors contained NA and were removed. +A total of `r length(gp$pred.is.const)` predictors were constant and were removed. +A total of `r length(unique(unlist(gp$pred.groups)))` predictors formed `r length(gp$pred.groups)` groups. +These are the groups and the members: +```{r, echo=FALSE} +gp$pred.groups +``` + +# Re: prior +These are details rearding the prior with the name `r names(IN$priors)[1]` after grouping predictors. +```{r} +table(p.mat) +dim(p.mat) + +# table of out-degree and counts +out.vec <- apply(p.mat != 0, 2, sum) +table(out.vec) +# show how many regulators (TFs) have targets +table(out.vec > 0) + +# print the regulators with the most targets +mt <- order(out.vec, decreasing=TRUE)[1:5] +data.frame(colnames(p.mat)[mt], out.vec[mt]) + +# table of in-degree and counts +in.vec <- apply(p.mat != 0, 1, sum) +table(in.vec) +# show how many targets have regulators +table(in.vec > 0) + +# print the targets with the most regulators +mt <- order(in.vec, decreasing=TRUE)[1:5] +data.frame(rownames(p.mat)[mt], in.vec[mt]) + +# print some network statisctics +net.stats(p.mat) + +# overlap of prior and final network (beta sign) +# only use TFs and targets in the prior +rows <- in.vec > 0 +cols <- out.vec > 0 + +overlap.tab <- table(as.vector(abs(beta.sign)[rows, cols] >= BETA.SIGN.TH), as.vector(p.mat[rows, cols] != 0)) +overlap.tab +fisher.test(overlap.tab, alternative='greater') +# consider sign of interactions +table(as.vector(abs(beta.sign)[rows, cols] >= BETA.SIGN.TH) * sign(as.vector(beta.sign[rows, cols])), as.vector(sign(p.mat[rows, cols]))) + +# overlap of prior and final network (explained variance) +overlap.tab <- table(as.vector(var.exp[rows, cols]) >= VAR.EXP.TH, as.vector(p.mat[rows, cols] != 0)) +overlap.tab +fisher.test(overlap.tab, alternative='greater') + +``` + + +# Re: AUPR + +```{r} +source('/home/ch1421/Projects/Inferelator/R_scripts/evaluate.R') +cc.aupr <- aupr(comb.confs, p.mat, eval.on.subset=TRUE) +cat('AUPR is', cc.aupr, '\n') + +``` + + + + + + diff --git a/Inferelator/R_scripts/net_report_new.R b/Inferelator/R_scripts/net_report_new.R new file mode 100644 index 0000000..4d7e40d --- /dev/null +++ b/Inferelator/R_scripts/net_report_new.R @@ -0,0 +1,20 @@ + +net.report <- function(cc.file) { + require('markdown') + require('knitr') + + cc.dir <- normalizePath(dirname(cc.file)) + + mdf <- normalizePath('R_scripts/net_report.Rmd') + kmdf <- file.path(cc.dir, 'net_report.md') + htf <- file.path(cc.dir, 'net_report.html') + + # knit + opts_knit$set(base.dir=cc.dir, width=112) + opts_chunk$set(fig.path=file.path(cc.dir, 'report_figures/'), fig.width=14, fig.height=8) + knit(mdf, kmdf) + + # render markdown + markdownToHTML(kmdf, htf, options=c('base64_images', 'highlight_code')) + file.remove(kmdf) +} diff --git a/Inferelator/R_scripts/priors.R b/Inferelator/R_scripts/priors.R new file mode 100644 index 0000000..5b42d59 --- /dev/null +++ b/Inferelator/R_scripts/priors.R @@ -0,0 +1,164 @@ +library('Matrix') + +getPriors <- function(exp.mat, tf.names, priors.mat, gs.mat, eval.on.subset, + job.seed, perc.tp, perm.tp, perc.fp, perm.fp, sel.mode) { + # if no priors.mat matrix is given, generate an empty one + if (is.null(priors.mat)) { + priors.mat <- Matrix(0, nrow(exp.mat), length(tf.names), + dimnames=list(rownames(exp.mat), tf.names)) + } + + # priors.pars is list of prior parameters; every entry is a vector of four + # elements: perc.tp, tp permutation number, perc.fp, fp permutation number + + priors.pars <- list() + + # Note: We require the perc.*p and perm.*p vectors to be of the same length as + # in the following code only the same positions of the vectors will be paired. + + lengths <- sapply(list(perc.tp, perm.tp, perc.fp, perm.fp), length) + if (length(unique(lengths)) != 1) { + stop("Error parsing prior parameters: perc.tp, perm.tp, perc.fp, perm.fp \ + don't have the same length.") + } + + for (pos in 1:unique(lengths)) { + + # there need not be the same number of permutations for tp and fp + # but we have to make shure that the smaller one can be repeated to match + # the other one + + rmndr <- max(perm.tp[pos], perm.fp[pos]) %% min(perm.tp[pos], perm.fp[pos]) + if (rmndr != 0) { + stop("Error parsing prior parameters: Larger number of permutations is \ + not multiple of smaller number.") + } + + priors.pos <- cbind(perc.tp[pos], 1:perm.tp[pos], perc.fp[pos], 1:perm.fp[pos]) + + # every row represents one combination of priors we want to test; + # append to list of priors parameters + priors.pars <- c(priors.pars, as.list(as.data.frame(t(priors.pos)))) + + } + + priors <- list() + for (i in 1:length(priors.pars)) { + pp <- priors.pars[[i]] + + priors[[i]] <- Matrix(0, nrow(priors.mat), ncol(priors.mat), dimnames=dimnames(priors.mat)) + names(priors)[i] <- paste('frac_tp_', pp[1], '_perm_', pp[2], '--frac_fp_', + pp[3], '_perm_', pp[4], sep="") + + if (pp[1] == 100 & pp[3] == 0) { + priors[[i]] <- priors.mat + } + else if (pp[1] > 0 | pp[3] > 0) { + priors[[i]] <- getPriorMatrix(priors.mat, pp, gs.mat, eval.on.subset, + job.seed, sel.mode) + } + + } + return(priors) +} + +getPriorMatrix <- function(priors, prior.pars, gs, from.subset, seed, sel.mode) { + # Given the gold standard, path of the input data, and a set of prior + # parameters, this function returns a single -1, 0, 1 matrix of random + # priors. + + perc.tp <- prior.pars[1] + perm.tp <- prior.pars[2] + perc.fp <- prior.pars[3] + perm.fp <- prior.pars[4] + + if (!from.subset) { + if (sel.mode == 'random') { + p.mat <- makePriorMat(priors, perc.tp, perm.tp + seed) + + makePriorMat(priors, perc.fp, perm.fp + seed, false.priors = TRUE) + } + if (sel.mode == 'tf') { + p.mat <- makePriorMatTF(priors, perc.tp, perm.tp + seed) + } + } else { + p.mat <- Matrix(0, nrow(priors), ncol(priors)) + rows <- apply(gs != 0, 1, sum) > 0 + cols <- apply(gs != 0, 2, sum) > 0 + p.mat[rows, cols] <- getPriorMatrix(priors[rows, cols], prior.pars, NULL, + FALSE, seed, sel.mode) + } + dimnames(p.mat) <- dimnames(priors) + return(p.mat) + +} + +makePriorMat <- function(priors, perc, perm, false.priors = FALSE) { + # Creates prior matrix based on: + # + # priors - matrix with priors + # perc - percentage of priors to use + # perm - the permutation of this priors-perc combination + # false.priors - whether to use false priors + # + # Notes: By setting the RNG seed using perm, we make sure that, for a given + # priors-perm combination, matrices with higher perc values include all the + # priors of those with lower perc values. + + # save the state of the RNG + rng.state <- .Random.seed + + set.seed(perm, "Mersenne-Twister", "Inversion") + + n.priors <- floor(sum(priors != 0) * perc / 100) + p.mat <- Matrix(0, nrow(priors), ncol(priors)) + + if (n.priors > 0) { + if (false.priors) { + prior.order <- sample(which(priors == 0)) + p.mat[prior.order[1:n.priors]] <- sample(priors[priors != 0], n.priors, replace=TRUE) + } else { + prior.order <- sample(which(priors != 0)) + p.mat[prior.order[1:n.priors]] <- priors[prior.order[1:n.priors]] + } + + # The above code ignores whether perc is set too high, but we should warn + # the user. + if (n.priors > length(prior.order)) { + warning("Percent of priors set too high. Only the max used.", call.=TRUE) + } + } + + # return RNG to old state + .Random.seed <<- rng.state + + return(p.mat) +} + +makePriorMatTF <- function(priors, perc, perm, false.priors = FALSE) { + # Creates prior matrix based on: + + # save the state of the RNG + rng.state <- .Random.seed + + set.seed(perm, "Mersenne-Twister", "Inversion") + + # how many TFs do we pick + tf.out.deg <- apply(priors != 0, 2, sum) + n.tfs <- floor(sum(tf.out.deg > 0) * perc / 100) + p.mat <- Matrix(0, nrow(priors), ncol(priors)) + + if (n.tfs > 0) { + if (false.priors) { + stop('False priors not supported in all-or-nothing TF mode') + } else { + tf.order <- sample(which(tf.out.deg > 0)) + p.mat[, tf.order[1:n.tfs]] <- priors[, tf.order[1:n.tfs]] + } + } + + # return RNG to old state + .Random.seed <<- rng.state + + return(p.mat) +} + diff --git a/Inferelator/R_scripts/summarize_results.R b/Inferelator/R_scripts/summarize_results.R new file mode 100644 index 0000000..c6aff59 --- /dev/null +++ b/Inferelator/R_scripts/summarize_results.R @@ -0,0 +1,134 @@ +library('Matrix') +source('R_scripts/evaluate.R') + +get.mean.and.lh <- function(mat) { + ret <- list() + ret$mean <- apply(mat, 1, mean) + ret$median <- apply(mat, 1, median) + tmp <- apply(mat, 1, quantile, probs=c(0.05, 0.95)) + ret$low <- tmp[1, ] + ret$high <- tmp[2, ] + return(ret) +} + +to.confidence.score <- function(mat) { + tmp <- rank(mat) + return(1 - (tmp - 1) / (max(tmp) - 1)) +} + +sum.net <- function(betas, betas.resc, comb.confs, IN, cc.file, th=0.5) { + + beta.sign <- as.matrix(betas[[1]] * 0) + beta.non.zero <- as.matrix(betas[[1]] * 0) + for (n in 1:length(betas)) { + beta.sign <- beta.sign + sign(betas[[n]]) + beta.non.zero <- beta.non.zero + (betas[[n]] != 0) + } + dimnames(beta.sign) <- dimnames(comb.confs) + dimnames(beta.non.zero) <- dimnames(comb.confs) + + # we only care about interactions that are present in more than th (fraction) bootstraps + non.zero <- which(beta.non.zero > (length(betas) * th), arr.ind=TRUE) + vec.ind <- which(beta.non.zero > (length(betas) * th)) + + # get mean beta score + betas.bs <- lapply(betas, function(x) x[vec.ind]) + betas.bs <- matrix(unlist(betas.bs), length(betas.bs[[1]])) + betas.stats <- get.mean.and.lh(betas.bs) + rm(betas.bs) + + # get mean rescaled beta + resc.betas <- lapply(betas.resc, function(x) x[vec.ind]) + resc.betas <- matrix(unlist(resc.betas), length(resc.betas[[1]])) + resc.betas.stats <- get.mean.and.lh(resc.betas) + + # get mean of ranks + resc.betas.ranks <- apply(-resc.betas, 2, rank) + resc.betas.ranks.stats <- get.mean.and.lh(resc.betas.ranks) + rm(resc.betas, resc.betas.ranks) + + # get confidence scores + conf.scores <- lapply(betas.resc, function(x) to.confidence.score(-as.matrix(x))[vec.ind]) + conf.scores <- matrix(unlist(conf.scores), length(conf.scores[[1]])) + conf.scores.stats <- get.mean.and.lh(conf.scores) + rm(conf.scores) + + # match the ccfile to the prior that was used + p.ind <- which(sapply(names(IN$priors), function(n) grepl(n, cc.file))) + p.name <- names(IN$priors)[p.ind] + p.mat <- IN$grouped.pred[[p.name]]$prior.mat[rownames(beta.sign), colnames(beta.sign)] + + gp <- IN$grouped.pred[[p.name]] + cat('A total of', length(gp$pred.has.na), 'predictors contained NA and were removed.\n') + cat('A total of', length(gp$pred.is.const), 'predictors were constant and were removed.\n') + cat('A total of', length(unique(unlist(gp$pred.groups))), 'predictors formed', length(gp$pred.groups), 'groups.\n') + source('R_scripts/evaluate.R') + cc.aupr <- aupr(comb.confs, p.mat, eval.on.subset=TRUE) + cat('AUPR is', cc.aupr, '\n') + + # create data frame of non-zero scores + net.df <- data.frame(regulator=colnames(beta.sign)[non.zero[, 2]], + target=rownames(beta.sign)[non.zero[, 1]], + beta.sign.sum=beta.sign[vec.ind], + beta.non.zero=beta.non.zero[vec.ind] / length(betas), + beta.low=betas.stats$low, + beta.mean=betas.stats$mean, + beta.high=betas.stats$high, + var.exp.low=resc.betas.stats$low, + var.exp.median=resc.betas.stats$median, + var.exp.mean=resc.betas.stats$mean, + var.exp.high=resc.betas.stats$high, + var.exp.rank.low=resc.betas.ranks.stats$low, + var.exp.rank.mean=resc.betas.ranks.stats$mean, + var.exp.rank.high=resc.betas.ranks.stats$high, + conf.score.low=conf.scores.stats$low, + conf.score.mean=conf.scores.stats$mean, + conf.score.high=conf.scores.stats$high, + prior=p.mat[vec.ind]) + + iao <- order(-net.df$conf.score.mean, net.df$var.exp.rank.mean, -net.df$var.exp.mean, -abs(net.df$beta.non.zero), net.df$regulator, net.df$target) + net.df <- net.df[iao, ] + + net.df$precision <- cumsum((net.df$prior != 0)) / (1:length(net.df$prior)) + + fn <- gsub('combinedconf', 'summary', cc.file) + save(net.df, file=fn) + fn <- gsub('.RData', '.tsv', fn) + tmp.net.df <- net.df[net.df$beta.non.zero >= 0.5, ] + for (i in 3:ncol(tmp.net.df)) { + tmp.net.df[, i] <- round(tmp.net.df[, i], 3) + } + write.table(tmp.net.df, file=fn, sep='\t', row.names=FALSE, quote=FALSE) +} + +sum.net.cc <- function(cc.file) { + in.dir <- dirname(cc.file) + + load(cc.file) + load(gsub('combinedconf', 'betas', cc.file)) + load(paste(in.dir, 'params_and_input.RData', sep='/')) + + sum.net(betas, betas.resc, comb.confs, IN, cc.file) +} + + +#v13.medium <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_1505_upstr_or_0_9_50_adjpth001_incl_autoreg_filtered_16_61224_518_3761_tfa_Peking_plus_lit_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' +#sum.net.cc(v13.medium) +#sum.net.cc('output/tmp_201502_final_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData') +#sum.net.cc('output/bsubtilis_us_201506_stfa_noself3_filtered_newresp_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData') +#sum.net.cc('output/bsubtilis_eu_201506_stfa_noself3_filtered_newresp_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData') + +#eu.allpr <- 'output/bsubtilis_eu_201506_allpr_noself3_filtered_newresp_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#us.allpr <- 'output/bsubtilis_us_201502_final_allpr_BBSR_1.1_100_0/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' + +#v21.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364_BBSR_1.1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#v22.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364_sspr_BBSR_1.1_33/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' + +#v23.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364_462tfs_BBSR_1.1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#v24.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364__462tfs_sspr_BBSR_1.1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#v24.small.pr.41bs <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364_lesstfs_sspr_BBSR_1.1_41/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#cc.v31 <- '/home/ch1421/Projects/Rice/inferelator_output/151013_590bp_open_expr_adjpth001_exprtfs_incl_autoreg_merged005_filtered_32_sspr_BBSR_1.1_201/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' + +#sum.net.cc(v23.small.pr) +#sum.net.cc(cc.v31) + diff --git a/Inferelator/R_scripts/tfa.R b/Inferelator/R_scripts/tfa.R new file mode 100644 index 0000000..f7e40d3 --- /dev/null +++ b/Inferelator/R_scripts/tfa.R @@ -0,0 +1,140 @@ + +# fix prior known interactions so the pseudoinverse does not fail +fix.pki <- function(pki) { + diag(pki)[apply(pki != 0, 2, sum) == 0] <- 1 + # check for identical columns in pki + m <- ncol(pki) + fix.me <- T + while (fix.me) { + fix.me <- F + for (i in which(duplicated(pki, MARGIN=2))) { + for (j in 1:(i-1)) { + if(all(pki[, i] == pki[, j]) == TRUE) { + pki[i, i] <- 1 + pki[j, j] <- 1 + cat('Identical columns in rf (', i, j, ') fixed via self-interactions.\n') + fix.me <- T + } + } + } + } + return(pki) +} + + +# if noself is TRUE, all self-regulatory interactions are removed +# however, if dup.self is TRUE, self interactions for TFs that other TFs with the +# exact same set of interactions in the prior are kept +# the motivation for dup.self=TRUE is that TFs with identical prior should have +# identical TFA +tfa <- function(prior, exp.mat, exp.mat.halftau, noself=TRUE, dup.self=TRUE) { + tfwt <- apply(prior != 0, 2, sum) > 0 + + duplicates <- c() + if (dup.self) { + duplicates <- duplicated(prior[, tfwt], MARGIN=2) | + duplicated(prior[, tfwt], MARGIN=2, fromLast = TRUE) + duplicates <- colnames(prior)[tfwt][duplicates] + } + + tfs <- setdiff(colnames(prior), duplicates) + tfs <- intersect(tfs, rownames(prior)) + if (noself) { + diag(prior[tfs, tfs]) <- 0 + } + + activities <- matrix(0, ncol(prior), ncol(exp.mat.halftau), + dimnames=list(colnames(prior), colnames(exp.mat.halftau))) + + if (any(tfwt)) { + require('corpcor') + activities[tfwt, ] <- pseudoinverse(prior[, tfwt, drop=FALSE]) %*% exp.mat.halftau + } + + use.exp <- intersect(colnames(prior)[!tfwt], rownames(exp.mat)) + activities[use.exp, ] <- exp.mat[use.exp, ] + + return(activities) +} + + +tfa.bs <- function(prior, res.mat, des.mat) { + K <- 50 + act.bs <- array(NA, dim=c(ncol(prior), ncol(res.mat), K)) + cond <- ncol(res.mat) + for (k in 1:K) { + selected <- sample(cond, replace=T) + activities <- tfa(prior, res.mat[, selected], des.mat[, selected]) + act.bs[,selected,k] <- activities + } + ret <- apply(act.bs, 1:2, median, na.rm=TRUE) + dimnames(ret) <- dimnames(activities) + return(ret) +} + +tfa.nn <- function(prior, res.mat, des.mat, cores) { + require('nnls') + diag(prior) <- 0 + #cor.mat <- cor(t(res.mat), t(res.mat[colnames(prior), ])) + #prior <- prior * sign(cor.mat) + #prior.fixed <- fix.pki(prior) + + #dn <- dimnames(prior) + #prior <- refit.betas.mc(des.mat[colnames(prior), ], res.mat, prior)[, -1] + #dimnames(prior) <- dn + prior.fixed <- fix.pki(prior) + + #abs.col.avg <- apply(abs(prior.fixed), 2, sum) / apply(prior.fixed != 0, 2, sum) + #abs.col.avg <- apply(prior.fixed, 2, sum) / apply(prior.fixed != 0, 2, sum) + #prior.fixed <- t(t(prior.fixed) / abs.col.avg) + + des.mat <- matrix(0, ncol(prior), ncol(res.mat), dimnames=list(colnames(prior), colnames(res.mat))) + for (i in 1:ncol(res.mat)) { + des.mat[, i] <- nnls(prior.fixed, res.mat[, i])$x + } + return(des.mat) +} + +collapse.activities <- function(activities, prior) { + tfs <- colnames(prior) + prior.noself <- prior + diag(prior.noself[tfs, tfs]) <- 0 + noself.targets <- apply(prior.noself != 0, 2, sum) + + dup <- which(duplicated(t(prior)) & apply(prior != 0, 2, sum) > 0) + while (length(dup) > 0) { + dup.cols <- which(apply(prior, 2, function(x) all(x==prior[, dup[1]]))) + exemplar <- dup.cols[order(noself.targets[dup.cols], decreasing=TRUE)[1]] + print('collapse activities') + print(dup) + print(dup.cols) + print(exemplar) + for (dup.col in dup.cols) { + #activities[dup.cols, ] <- apply(activities[dup.cols, ], 2, mean) + activities[dup.col, ] <- activities[exemplar, ] + } + dup <- setdiff(dup, dup.cols) + } + return(activities) +} + +# given a design and response matrix, refit the betas +# useful if we don't know whether the old betas came from scaled design and +# response matrices +refit.betas.mc <- function(X, Y, betas.old, cores) { + X <- rbind(1, X) + beta.rows <- mclapply(1:nrow(Y), refit.one, X, Y, betas.old, mc.cores=cores) + beta <- matrix(unlist(beta.rows), nrow(Y), byrow=TRUE) + return(beta=beta) +} + +refit.one <- function(i, X, Y, betas.old) { + K <- nrow(X) + beta <- rep(0, K) + selected <- c(TRUE, betas.old[i, ] != 0) + x <- t(matrix(X[selected, ], sum(selected))) + coefs <- as.numeric(solve(crossprod(x), crossprod(x, Y[i, ]))) + beta[selected] <- coefs + return(beta) +} + diff --git a/Inferelator/R_scripts/utils.R b/Inferelator/R_scripts/utils.R new file mode 100644 index 0000000..568fbb8 --- /dev/null +++ b/Inferelator/R_scripts/utils.R @@ -0,0 +1,115 @@ +# given an expression matrix, create a trivial cluster stack - no (bi)clusters +trivial.cluster.stack <- function(exp.mat) { + clusterStack <- list() + for (i in 1:nrow(exp.mat)) { + clusterStack[[i]] <- list(cols=NA, ncols=NA, rows=NA, nrows=NA, resid=NA, k=NA, redExp=NA) + clusterStack[[i]]$cols <- colnames(exp.mat) + clusterStack[[i]]$ncols <- ncol(exp.mat) + clusterStack[[i]]$rows <- rownames(exp.mat)[i] + clusterStack[[i]]$nrows <- 1 + clusterStack[[i]]$k <- i + clusterStack[[i]]$redExp <- exp.mat[i, ] + } + return(clusterStack) +} + +# given the condition names, create meta data data frame that assumes all +# observations are steady state measurements +trivial.meta.data <- function(cond.names) { + meta.data <- data.frame(condName=cond.names) + meta.data$isTs <- FALSE + meta.data$is1stLast <- 'e' + meta.data$prevCol <- NA + meta.data$del.t <- NA + return(meta.data) +} + +reshape.prior <- function(priors.mat, gene.names, tf.names) { + bg.val <- as.numeric(names(which.max(table(priors.mat)))) + res <- matrix(bg.val, length(gene.names), length(tf.names), + dimnames=list(gene.names, tf.names)) + p.genes <- intersect(rownames(priors.mat), gene.names) + p.tfs <- intersect(colnames(priors.mat), tf.names) + res[p.genes, p.tfs] <- priors.mat[p.genes, p.tfs] + return(res) +} + +read.input <- function(input.dir, exp.mat.file, tf.names.file, meta.data.file, + priors.file, gold.standard.file, leave.out.file, randomize.expression) { + IN <- list() + + cat('Reading input: expression matrix') + if (grepl('.RData$', exp.mat.file)) { + IN$exp.mat <- as.matrix(local(get(load(file.path(input.dir, exp.mat.file))))) + } else { + IN$exp.mat <- as.matrix(read.table(file=file.path(input.dir, exp.mat.file), + row.names=1, header=T, sep='\t', check.names=F)) + } + + cat(', TF names') + if (grepl('.RData$', tf.names.file)) { + IN$tf.names <- local(get(load(file.path(input.dir, tf.names.file)))) + } else { + IN$tf.names <- unique(as.vector(as.matrix(read.table(file.path(input.dir, tf.names.file))))) + } + IN$tf.with.expr <- IN$tf.names[IN$tf.names %in% rownames(IN$exp.mat)] + + cat(', meta data') + IN$meta.data <- NULL + if (!is.null(meta.data.file)) { + if (grepl('.RData$', meta.data.file)) { + IN$meta.data <- local(get(load(file.path(input.dir, meta.data.file)))) + } else { + IN$meta.data <- read.table(file=file.path(input.dir, meta.data.file), + header=T, sep='\t', check.names=F) + } + } + + cat(', leave-out file') + # if there is a leave-out file, ignore some conditions + if (!is.null(leave.out.file)) { + leave.out <- as.vector(as.matrix(read.table(file.path(input.dir, leave.out.file)))) + cat('Leaving out the following conditions:', leave.out, '\n') + cat(sprintf('%d of %d are present in the expression data\n', + sum(leave.out %in% colnames(IN$exp.mat)), length(leave.out))) + lo <- (IN$meta.data$prevCol %in% leave.out) | (IN$meta.data$condName %in% leave.out) + cat('Total number of conditions being dropped:', sum(lo), '\n') + IN$meta.data.lo <- IN$meta.data[lo, ] + IN$meta.data <- IN$meta.data[!lo, ] + IN$exp.mat.lo <- IN$exp.mat[, as.character(IN$meta.data.lo$condName)] + IN$exp.mat <- IN$exp.mat[, as.character(IN$meta.data$condName)] + } + + cat(', priors matrix') + IN$priors.mat <- NULL + if (!is.null(priors.file)) { + if (grepl('.RData$', priors.file)) { + IN$priors.mat <- as.matrix(local(get(load(file.path(input.dir, priors.file))))) + } else { + IN$priors.mat <- as.matrix(read.table(file=file.path(input.dir, priors.file), + row.names=1, header=T, sep='\t', check.names=F)) + } + IN$priors.mat <- reshape.prior(IN$priors.mat, rownames(IN$exp.mat), IN$tf.names) + } + + cat(', gold standard matrix') + IN$gs.mat <- NULL + if (!is.null(gold.standard.file)) { + if (grepl('.RData$', gold.standard.file)) { + IN$gs.mat <- as.matrix(local(get(load(file.path(input.dir, gold.standard.file))))) + } else { + IN$gs.mat <- as.matrix(read.table(file=file.path(input.dir, gold.standard.file), + row.names=1, header=T, sep='\t', check.names=F)) + } + IN$gs.mat <- reshape.prior(IN$gs.mat, rownames(IN$exp.mat), IN$tf.names) + } + cat(' ... done.\n') + + if (randomize.expression) { + cat('randomize.expression is set to TRUE; randomizing expression matrix ...') + IN$exp.mat[sample(length(IN$exp.mat))] <- as.numeric(IN$exp.mat) + cat(' done.\n') + } + return(IN) +} + diff --git a/Inferelator/R_scripts/vis_res_tfa.R b/Inferelator/R_scripts/vis_res_tfa.R new file mode 100644 index 0000000..3396ccd --- /dev/null +++ b/Inferelator/R_scripts/vis_res_tfa.R @@ -0,0 +1,87 @@ +library('Matrix') +source('R_scripts/priors.R') + +ChristophsPR <- function(ord.idx, gs) { + prec <- cumsum(gs[ord.idx]) / cumsum(rep(1, length(ord.idx))) + rec <- cumsum(gs[ord.idx]) / sum(gs) + + prec <- c(prec[1], prec) + rec <- c(0, rec) + + auc <- ChristophsAUC(rec, prec) + return(list(prec=prec, rec=rec, auc=auc)) +} + + +ChristophsAUC <- function(x, y) { + dx <- diff(x) + my <- y[1:(length(y) - 1)] + diff(y) / 2 + return(sum(dx * my)) +} + + +auprs <- function(mat, gs, prior, eval.on.subset=FALSE) { + rows <- rep(TRUE, nrow(gs)) + cols <- rep(TRUE, ncol(gs)) + if (eval.on.subset) { + rows <- apply(gs, 1, sum) > 0 + cols <- apply(gs, 2, sum) > 0 + } + mat <- mat[rows, cols] + gs <- gs[rows, cols] + prior <- prior[rows, cols] + + total <- ChristophsPR(order(mat, decreasing=TRUE), gs)$auc + trainset <- ChristophsPR(order(mat[prior!=0], decreasing=TRUE), gs[prior!=0])$auc + testset <- ChristophsPR(order(mat[prior==0], decreasing=TRUE), gs[prior==0])$auc + return(c(total, trainset, testset)) +} + + +dirs <- c('/home/ch1421/Projects/Inferelator/output/bsusnew_spc_tfabs_tp50_nomd_60_15_15_BBSR_1_FALSE/', + '/home/ch1421/Projects/Inferelator/output/bsusnew_spc_tfabs_tp50_nomd_60_15_15_BBSR_1.1_FALSE/', + '/home/ch1421/Projects/Inferelator/output/bsusnew_spc_tfabs_tp50_nomd_60_15_15_BBSR_1.6_FALSE/', + '/home/ch1421/Projects/Inferelator/output/bsusnew_spc_tfabs_tp50_nomd_60_15_15_BBSR_1_TRUE/', + '/home/ch1421/Projects/Inferelator/output/bsusnew_spc_tfabs_tp50_nomd_60_15_15_BBSR_1.1_TRUE/', + '/home/ch1421/Projects/Inferelator/output/bsusnew_spc_tfabs_tp50_nomd_60_15_15_BBSR_1.6_TRUE/') + +res <- c() +for (dir in dirs) { + print(dir) + files <- list.files(dir, "combinedconf_.+\\.RData$") + load(paste(dir, 'params_and_input.RData', sep='')) + + .Random.seed <- SEED + weight <- PARS$prior.weight + tfa <- PARS$use.tfa + eval.on.subset <- PARS$eval.on.subset + gs <- IN$gs.mat + + priors <- getPriors(IN$exp.mat, IN$tf.names, IN$priors.mat, IN$gs.mat, + PARS$eval.on.subset, PARS$job.seed, PARS$perc.tp, + PARS$perm.tp, PARS$perc.fp, PARS$perm.fp) + + prior.order <- sapply(names(priors), function(x) which(grepl(x, files))) + files <- files[prior.order] + + for (i in 1:length(files)) { + res.file <- files[i] + load(file.path(dir, res.file)) + prior <- priors[[i]] + aupr.vals <- auprs(comb.confs, gs, prior, eval.on.subset) + print(aupr.vals) + res <- rbind(res, c(weight, tfa, aupr.vals)) + } +} + +colnames(res) <- c('weight', 'tfa', 'aupr.tot', 'aupr.tr', 'aupr.te') +res <- data.frame(res) +res$aupr.tot <- as.numeric(res$aupr.tot) +res$aupr.te <- as.numeric(res$aupr.te) + +library(ggplot2) +library(reshape2) + +dfm = subset(melt(res, id.vars=c('weight', 'tfa')), variable!='aupr.tr') + +ggplot(dfm, aes(x=as.factor(weight), y=value)) + geom_point(size = 4, position=position_dodge(width=0.5), aes(color = as.factor(tfa), shape = variable)) diff --git a/Inferelator/R_scripts/vis_tfs_and_targets.R b/Inferelator/R_scripts/vis_tfs_and_targets.R new file mode 100644 index 0000000..839b348 --- /dev/null +++ b/Inferelator/R_scripts/vis_tfs_and_targets.R @@ -0,0 +1,230 @@ +library('Matrix') +library('ggplot2') +library('reshape2') +library('gplots') +if ('parallel' %in% installed.packages()[, 'Package']) { + library('parallel') +} else { + library('multicore') +} + +# for heatmaps +my.hclust <- function(d) hclust(d, method="ward.D") +my.hclust.co <- function(d) hclust(d, method="complete") +my.hclust.av <- function(d) hclust(d, method="average") +dist.p.cor <- function(x) as.dist(1 - cor(t(x), method='pearson')) +dist.p.cor.abs <- function(x) as.dist(1 - abs(cor(t(x), method='pearson'))) +dist.s.cor <- function(x) as.dist(1 - cor(t(x), method='spearman')) +my.cols <- colorRampPalette(c("#053061", "#f7f7f7", "#67001f"))(31) +my.cols.BrBG <- colorRampPalette(c("#a6611a", "#f5f5f5", "#018571"))(31) + +get.cols <- function(val.min, val.median, val.max, col.min="#a6611a", col.median="#f5f5f5", col.max="#018571", N=32) { + seg1 <- val.median - val.min + seg2 <- val.max - val.median + seg.tot <- val.max - val.min + steps1 <- round(seg1 / seg.tot * N, 0) + steps2 <- round(seg2 / seg.tot * N, 0) + pal1 <- colorRampPalette(c(col.min, col.median))(steps1) + pal2 <- colorRampPalette(c(col.median, col.max))(steps2) + pal <- c(pal1, pal2) + return(pal[!duplicated(pal)]) +} + +plot.tf <- function(des.mat, res.mat, exp.mat, tf, net, prior, tfa.mat, fn, width=18, height=9, dpi=100) { + #if (file.exists(fn)) return(NULL) + targets <- rownames(net) + net.tar <- targets[net[, tf] != 0] + p.tar <- targets[prior[targets, tf] != 0] + in.net <- targets[apply(net != 0, 1, sum) > 0] + + p.tar <- setdiff(p.tar, tf) + p.no <- setdiff(p.tar, net.tar) + novel.tar <- setdiff(net.tar, p.tar) + p.yes <- intersect(p.tar, net.tar) + p.no.in.net <- intersect(p.no, in.net) + + cat(tf, 'targets', length(net.tar), 'priors', length(p.tar), 'not pred', length(p.no), 'novel', length(novel.tar), 'pred', length(p.yes)) + cat('\n') + + if (!is.null(tfa.mat)) { + des.mat <- tfa.mat + } + + # subset the expression matrix since it may have more columns than design and response + # (the last points of time series are not in design and response) + exp.mat <- exp.mat[rownames(res.mat), colnames(res.mat)] + + data.df <- melt(t(res.mat[union(net.tar, p.tar), , drop=FALSE]), varnames=c('sample', 'gene')) + data.df$sample <- factor(data.df$sample, levels=colnames(res.mat), ordered=TRUE) + data.df$type <- sprintf('novel targets (n=%d)', length(novel.tar)) + data.df$type[data.df$gene %in% p.no] <- sprintf('priors not in net (n=%d)', length(p.no)) + data.df$type[data.df$gene %in% p.yes] <- sprintf('priors in net (n=%d)', length(p.yes)) + + tf.df <- data.frame(gene=tf, sample=colnames(des.mat), value=des.mat[tf,]) + + g <- ggplot(data.df, aes(x=sample, y=value, group=gene)) + + geom_line(aes(color=gene), alpha=0.2, size=2) + + scale_color_discrete(guide = FALSE) + + #geom_point(data=tfa.df, aes(x=sample, y=value, group=gene), color='black', size=2) + geom_line(data=tf.df, aes(x=sample, y=value, group=gene), color='black', size=1) + + if (tf %in% rownames(exp.mat)) { + tf.exp.df <- data.frame(gene=tf, sample=colnames(exp.mat), value=exp.mat[tf,]) + g <- g + geom_line(data=tf.exp.df, aes(x=sample, y=value, group=gene), color='black', size=0.5, linetype=2) + } + + g <- g + + facet_grid(type ~ .) + + ylab('Expression') + xlab('Sample') + theme_bw() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size = rel(0.6))) + + ggsave(fn, g, width=width, height=height, dpi=dpi) + + rm(data.df) + gc() + + # for the heatmap + fn.hm <- gsub('.png', '_heatmap.png', fn) + goi <- union(net.tar, p.tar) + if (length(goi) > 1) { + hm.res.mat <- res.mat[goi, , drop=FALSE] + cols <- rep('white', length(goi)) + cols[goi %in% novel.tar] <- '#33a02c' #'#b2df8a' + cols[goi %in% p.yes] <- '#1f78b4' #'#a6cee3' + cols[goi %in% p.no.in.net] <- 'lightgrey' + + vcols <- get.cols(min(hm.res.mat), median(hm.res.mat), max(hm.res.mat)) + png(fn.hm, width=1800, height=900) + heatmap.2(hm.res.mat, scale='none', trace='none', hclustfun=my.hclust, col=vcols, + distfun=dist.p.cor, keysize = 0.66, cexRow=0.4, cexCol=0.3, RowSideColors=cols, Colv=NA, + symbreaks=FALSE) + dev.off() + } + + # for the heatmap + fn.hm <- gsub('.png', '_heatmap_prior.png', fn) + goi <- p.tar + if (length(goi) > 1) { + hm.res.mat <- res.mat[goi, , drop=FALSE] + cols <- rep('white', length(goi)) + cols[goi %in% novel.tar] <- '#33a02c' #'#b2df8a' + cols[goi %in% p.yes] <- '#1f78b4' #'#a6cee3' + cols[goi %in% p.no.in.net] <- 'lightgrey' + + vcols <- get.cols(min(hm.res.mat), median(hm.res.mat), max(hm.res.mat)) + png(fn.hm, width=1800, height=900) + heatmap.2(hm.res.mat, scale='none', trace='none', hclustfun=my.hclust, col=vcols, + distfun=dist.p.cor, keysize = 0.66, cexRow=0.4, cexCol=0.3, RowSideColors=cols, Colv=NA, + symbreaks=FALSE) + dev.off() + } + + return(NULL) + #print(g) +} + + +plot.TFs.and.targets <- function(net, prior, des.mat, res.mat, exp.mat, out.dir, tfa.mat, CORES) { + tfs <- colnames(net) + targets <- rownames(net) + + tf.out.deg <- apply(net != 0, 2, sum) + tf.out.deg.p <- apply(prior[targets, tfs] != 0, 2, sum) + + ignore <- mclapply(which(tf.out.deg > 0), function(i) plot.tf(des.mat, res.mat, exp.mat, tfs[i], net, prior, tfa.mat, sprintf('%s/%s_%03d_%03d.png', out.dir, tfs[i], tf.out.deg[i], tf.out.deg.p[i])), mc.cores=CORES) + return(NULL) +} + + +get.net <- function(betas, betas.resc, th, method) { + if (method == 'frequency') { + beta.count <- as.matrix(betas[[1]] * 0) + for (n in 1:length(betas)) { + beta.count <- beta.count + as.matrix(betas[[n]] != 0) + } + net <- (beta.count > length(betas)*th) + 0 + } + if (method == 'var.exp') { + beta.non.zero <- as.matrix(betas.resc[[1]] * 0) + for (n in 1:length(betas.resc)) { + beta.non.zero <- beta.non.zero + (betas.resc[[n]] != 0) + } + vec.ind <- which(beta.non.zero != 0) + resc.betas <- lapply(betas.resc, function(x) x[vec.ind]) + resc.betas <- matrix(unlist(resc.betas), length(resc.betas[[1]])) + net <- as.matrix(betas.resc[[1]] * 0) + net[vec.ind] <- apply(resc.betas, 1, median) + net[net <= th] <- 0 + } + return(net) +} + + +vis.tfs.and.targets <- function(ccfile, CORES=4) { + in.dir <- dirname(ccfile) + + load(ccfile) + load(gsub('combinedconf', 'betas', ccfile)) + load(paste(in.dir, 'params_and_input.RData', sep='/')) + + #net <- get.net(betas, betas.resc, 0.2, 'var.exp') + net <- get.net(betas, betas.resc, 0.5, 'frequency') + + # match the ccfile to the prior that was used + p.ind <- which(sapply(names(IN$priors), function(n) grepl(n, ccfile))) + p.name <- names(IN$priors)[p.ind] + + out.dir <- file.path(in.dir, sprintf('TF_plots_%s', p.name)) + dir.create(out.dir) + + p.mat <- IN$grouped.pred[[p.name]]$prior.mat + + des.mat <- IN$grouped.pred[[p.name]]$des.mat # if TFA was used, this will be activities + des.mat <- t(scale(t(des.mat))) + res.mat <- t(scale(t(IN$final_response_matrix))) + + exp.mat <- IN$exp.mat + exp.mat <- t(scale(t(exp.mat))) + + tfa.mat <- NULL + #if (PARS$use.tfa) { + # tfa.mat <- IN$tf.activities[[p.name]] + # tfa.mat <- t(scale(t(tfa.mat))) + #} + + plot.TFs.and.targets(net, p.mat, des.mat, res.mat, exp.mat, out.dir, tfa.mat, CORES) +} + + +#ccfile <- '/home/ch1421/Projects/Kostya/inferelator_output/test_2587_conds_tfa_1_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' +#ccfile <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_hughes_plus_150326_v8_incl_autoreg_filtered_32_61101_264_7371_tfa_Peking_plus_lit_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' +#ccfile <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_hughes_plus_150327_v8_incl_autoreg_tfa_Peking_plus_lit_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' + +#v8.small <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_hughes_plus_150416_v8_incl_autoreg_filtered_32_61101_264_7371_tfa_Peking_plus_lit_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' +#v8.large <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_hughes_plus_150416_v8_incl_autoreg_tfa_Peking_plus_lit_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' +#v8.medium <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_hughes_plus_150416_v8_incl_autoreg_filtered_16_105631_340_13434_tfa_Peking_plus_lit_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' + +#d5.small <- '/home/ch1421/Projects/Inferelator/output/dream5_net1_small/combinedconf_frac_tp_0_perm_1--frac_fp_0_perm_1_1.RData' + +#v9.medium <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_1505_or_0_9_50_incl_autoreg_filtered_16_279593_519_6620_tfa_Peking_plus_lit_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' + +#v12.medium <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_1505_upstr_or_0_9_50_incl_autoreg_filtered_16_200513_546_6320_tfa_Peking_plus_lit_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' + +#v13.medium <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_1505_upstr_or_0_9_50_adjpth001_incl_autoreg_filtered_16_61224_518_3761_tfa_Peking_plus_lit_BBSR_1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.RData' +#v13.medium.pr <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_1505_upstr_or_0_9_50_adjpth001_incl_autoreg_filtered_16_61224_518_3761_tfa_Peking_plus_lit_BBSR_1.1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' + +#v16.medium.pr <- '/home/ch1421/Projects/Rice/inferelator_output/ALL_150616_upstr_or_0_9_50_adjpth001_incl_autoreg_filtered_16_49869_520_3741_tfa_Peking_plus_lit_BBSR_1.1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#v17.medium.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150616_upstr_or_0_9_50_adjpth001_incl_autoreg_filtered_16_64303_504_4004_BBSR_1.1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#v22.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364_sspr_BBSR_1.1_33/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' + +#v23.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364_462tfs_BBSR_1.1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#v24.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364__462tfs_sspr_BBSR_1.1_20/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#v2x.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364_lesstfs_sspr_BBSR_1.1_41/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#v26.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/150622_allmotifs_694bp_open_expr_adjpth001_incl_autoreg_merged005_filtered_32_47842_462_3364_lesstfs_sspr_BBSR_1.1_101/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' + +#v30.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/151013_590bp_open_expr_adjpth001_exprtfs_incl_autoreg_merged005_filtered_32_sspr_BBSR_1.1_41/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' + +#v31.small.pr <- '/home/ch1421/Projects/Rice/inferelator_output/151013_590bp_open_expr_adjpth001_exprtfs_incl_autoreg_merged005_filtered_32_sspr_BBSR_1.1_201/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' +#cc.v32 <- '/home/ch1421/Projects/Rice/inferelator_output/151013_590bp_open_expr_adjpth001_exprtfs_incl_autoreg_merged005_sspr_BBSR_1.1_201/combinedconf_frac_tp_100_perm_1--frac_fp_0_perm_1_1.1.RData' + +#vis.tfs.and.targets(v31.small.pr, CORES=4) + diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R new file mode 100644 index 0000000..a63eb1b --- /dev/null +++ b/Inferelator/inferelator.R @@ -0,0 +1,448 @@ +## Bonneau lab +## NYU - Center for Genomics and Systems Biology + +# Call this script with a job config file as arguments +# Example call: Rscript inferelator.R jobs/dream4_cfg.R + + +library('Matrix') + +rm(list=ls()) +gc() + +source('R_scripts/utils.R') +source('R_scripts/design_and_response.R') +source('R_scripts/priors.R') +source('R_scripts/mi_and_clr.R') +source('R_scripts/bayesianRegression.R') +source('R_scripts/men.R') +source('R_scripts/evaluate.R') +source('R_scripts/tfa.R') +source('R_scripts/group_predictors.R') +source('R_scripts/summarize_results.R') +source('R_scripts/vis_tfs_and_targets.R') + + +date.time.str <- format(Sys.time(), "%Y-%m-%d_%H-%M-%S") +print(date.time.str) +start.proc.time <- proc.time() + +# default job parameters +PARS <- list() + +PARS$input.dir <- 'input/dream4' + +PARS$exp.mat.file <- 'expression.tsv' +PARS$tf.names.file <- 'tf_names.tsv' +PARS$meta.data.file <- NULL +PARS$priors.file <- NULL +PARS$gold.standard.file <- NULL +PARS$leave.out.file <- NULL +PARS$randomize.expression <- FALSE + +PARS$job.seed <- 42 # set to NULL if a random seed should be used +PARS$save.to.dir <- NULL +PARS$num.boots <- 20 +PARS$max.preds <- 10 +PARS$mi.bins <- 10 +PARS$cores <- 8 + +PARS$delT.max <- 110 +PARS$delT.min <- 0 +PARS$tau <- 45 + +PARS$perc.tp <- 0 +PARS$perm.tp <- 1 +PARS$perc.fp <- 0 +PARS$perm.fp <- 1 +PARS$pr.sel.mode <- 'random' # prior selection mode: 'random' or 'tf' + +PARS$eval.on.subset <- FALSE + +PARS$method <- 'BBSR' # 'BBSR' or 'MEN' +PARS$prior.weight <- 1 + +PARS$use.tfa <- FALSE +PARS$prior.ss <- FALSE + +PARS$output.summary <- TRUE +PARS$output.report <- TRUE +PARS$output.tf.plots <- TRUE + + +# some of the elastic net parameters that are essentially constants; +# only override in config script if you know what you are doing +PARS$enet.sparseModels <- TRUE # sparser models +PARS$enet.nCv <- 10 # number of cross-validations +PARS$enet.lambda <- c(0, 1, 100) # l2 weights +PARS$enet.verbose <- FALSE # print progress to screen +PARS$enet.plot.it <- FALSE # generate cross-validation plots +PARS$enet.plot.file.name <- NULL # file name for plots + + +# input argument is the job config script which overrides the default parameters +args <- commandArgs(trailingOnly = TRUE) +if (length(args) == 1) { + job.cfg <- args[1] +} else { + #job.cfg <- 'jobs/bsubtilis_eu_201310_stfa_bbsr_22.R' + #job.cfg <- 'jobs/bsubtilis_eu_201310_stfa_bbsr_1_tp0_fp0.R' + job.cfg <- '/home/ch1421/Projects/Rice/inferelator_jobs/ALL_htseq_intersection-strict_motifprior_sspr.R' + #job.cfg <- '/home/ch1421/Projects/Emily/ILC_inferelator_job.R' + #job.cfg <- '/home/ch1421/Projects/Kostya/inferelator_jobs/test.R' + #job.cfg <- 'jobs/bsubtilis_eu_201310_stfa_bbsr_tf_11_tp50_fp0.R' + #job.cfg <- 'jobs/bsubtilis_us_201310_stfa_bbsr_11_tp100_fp0.R' + #job.cfg <- 'jobs/bsubtilits_eu_mario_20150420.R' + #job.cfg <- 'jobs/bsubtilis_us_synthetic.R' + #job.cfg <- 'jobs/bsubtilis_bbsr_tfa.R' + #job.cfg <- 'jobs/bsubtilis_us_201502_final_11_tp100_fp0_no_bkdR.R' +} + +# load job specific parameters from input config file +if (!is.null(job.cfg)) { + source(job.cfg) +} + + +# set the random seed +if(!is.null(PARS$job.seed)) { + set.seed(PARS$job.seed, "Mersenne-Twister", "Inversion") + cat("RNG seed has been set to ", PARS$job.seed, "\n") +} else { + ignore <- runif(1) +} +SEED <- .Random.seed + + +# read input data +IN <- read.input(PARS$input.dir, PARS$exp.mat.file, PARS$tf.names.file, + PARS$meta.data.file, PARS$priors.file, PARS$gold.standard.file, + PARS$leave.out.file, PARS$randomize.expression) + +# keep only TFs that are part of the expression data +#IN$tf.names <- IN$tf.names[IN$tf.names %in% rownames(IN$exp.mat)] + +# order genes so that TFs come before the other genes +#gene.order <- rownames(IN$exp.mat) +#gene.order <- c(gene.order[match(IN$tf.names, gene.order)], +# gene.order[which(!(gene.order %in% IN$tf.names))]) + +#IN$exp.mat <- IN$exp.mat[gene.order, ] +#if (!is.null(IN$priors.mat)) { +# IN$priors.mat <- IN$priors.mat[gene.order, IN$tf.names] +#} +#if (!is.null(IN$gs.mat)) { +# IN$gs.mat <- IN$gs.mat[gene.order, IN$tf.names] +#} + + +# no meta data given - assume all steady state measurements +if (is.null(IN$meta.data)) { + IN$meta.data <- trivial.meta.data(colnames(IN$exp.mat)) +} + + +# create dummy clusterStack - a real clusterStack is only needed when inferring +# on bi-clusters +clusterStack <- trivial.cluster.stack(IN$exp.mat) + + + + +if(is.null(PARS$save.to.dir)) { + PARS$save.to.dir <- file.path(PARS$input.dir, date.time.str) +} +cat("Output dir:", PARS$save.to.dir, "\n") +if (!file.exists(PARS$save.to.dir)){ + dir.create(PARS$save.to.dir, recursive=TRUE) +} else if (file.exists(paste(PARS$save.to.dir, "/params_and_input.RData", sep=""))) { + stop(sprintf('The output file %s already exists. Exiting.', paste(PARS$save.to.dir, "/params_and_input.RData", sep=""))) +} + + +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. +# create design and response matrix +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. + +cat("Creating design and response matrix ... ") +des.res <- design.and.response(IN$meta.data, IN$exp.mat, PARS$delT.min, + PARS$delT.max, PARS$tau) + +IN$final_response_matrix <- des.res$final_response_matrix +IN$final_design_matrix <- des.res$final_design_matrix +resp.idx <- des.res$resp.idx +cat("done.\n") + +if (!all(apply(resp.idx, 1, identical, resp.idx[1,]))) { + stop('This version of the Inferelator does not support biclusters. Sorry.') +} + + + +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. +# parse priors parameters and set up priors list +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. + + +cat("Setting up priors list ... ") +IN$priors <- getPriors(IN$exp.mat, IN$tf.names, IN$priors.mat, IN$gs.mat, + PARS$eval.on.subset, PARS$job.seed, PARS$perc.tp, + PARS$perm.tp, PARS$perc.fp, PARS$perm.fp, + PARS$pr.sel.mode) +cat("done.\n") + + +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. +# set up the bootstrap permutations +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. + + +cat("Setting up bootstrap permutations ... ") +IN$bs.pi <- matrix(0, nrow=PARS$num.boots, ncol=ncol(resp.idx)) +if (PARS$num.boots == 1) { + IN$bs.pi[1, ] <- resp.idx[1, ] +} else { + for (bootstrap in 1:PARS$num.boots) { + IN$bs.pi[bootstrap, ] <- resp.idx[1, sample(ncol(resp.idx), replace=TRUE)] + } +} +#IN$bs.prior.rm <- list() +#if (PARS$num.boots == 1) { +# IN$bs.prior.rm[[1]] <- c() +#} else { +# for (bootstrap in 1:PARS$num.boots) { +# IN$bs.prior.rm[[bootstrap]] <- setdiff(1:length(IN$priors[[1]]), sample(length(IN$priors[[1]]), replace=TRUE)) +# } +#} +cat("done.\n") + + +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. +# TFA specific initialization +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. + +if(PARS$use.tfa) { + + IN$tf.activities <- list() + cat("Setting up TFA specific response matrix ... ") + des.res <- design.and.response(IN$meta.data, IN$exp.mat, PARS$delT.min, + PARS$delT.max, PARS$tau/2) + + IN$final_response_matrix_halftau <- des.res$final_response_matrix + cat("done.\n") +} + + +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. +# main loop +## .-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-.***.-.-. +IN$grouped.pred <- list() +for (prior.name in names(IN$priors)) { + cat('Method:', PARS$method, '\nWeight:', PARS$prior.weight, '\nPriors:', + prior.name, '\n') + + prior <- as.matrix(IN$priors[[prior.name]]) + + # estimate transcription factor activities + if(PARS$use.tfa) { + prior.tf.activities <- tfa(prior, IN$final_design_matrix, IN$final_response_matrix_halftau) + IN$tf.activities[[prior.name]] <- prior.tf.activities + } + + des.mat <- IN$final_design_matrix[IN$tf.with.expr, ] + if(PARS$use.tfa) { + des.mat <- prior.tf.activities[IN$tf.names, ] + } + + cat("group predictors\n") + gp.out <- group.predictors(des.mat, prior, IN$gs.mat, IN$bs.pi, cor.th=0.99) + IN$grouped.pred[[prior.name]] <- gp.out + cat(sprintf('A total of %d predictors contained NA and were removed.\n', length(gp.out$pred.has.na))) + cat(sprintf('A total of %d predictors were constant and were removed.\n', length(gp.out$pred.is.const))) + cat(sprintf('A total of %d predictors formed %d groups.\n', length(unique(unlist(gp.out$pred.groups))), length(gp.out$pred.groups))) + + # above we have used the full prior to get activities and group predictors + + + IN$tf.activities.bs[[prior.name]] <- list() + #IN$grouped.pred.bs[[prior.name]] <- list() + betas <- list() + betas.resc <- list() + for (bootstrap in 1:PARS$num.boots) { + cat("Bootstrap", bootstrap, "of", PARS$num.boots, "\n") + + # set to FALSE to disable the next block + not.done <- PARS$prior.ss + gp.out.bs <- gp.out + while (not.done) { + not.done <- FALSE + + # set some of the prior interactions to zero + prior <- gp.out$prior.mat + to.zero <- setdiff(1:length(prior), sample(length(prior), replace=TRUE)) + cat(sprintf('Setting %d non-zero prior edges to zero\n', sum(prior[to.zero] != 0))) + prior[to.zero] <- 0 + + # estimate transcription factor activities + if(PARS$use.tfa) { + des.mat <- rbind(IN$final_design_matrix, group.expression(gp.out$pred.groups, IN$final_design_matrix)) + IN$tf.activities.bs[[prior.name]][[bootstrap]] <- tfa(prior, des.mat, IN$final_response_matrix_halftau) + } + + des.mat <- IN$final_design_matrix[IN$tf.with.expr, ] + if(PARS$use.tfa) { + des.mat <- IN$tf.activities.bs[[prior.name]][[bootstrap]][gp.out$tf.names, ] + } + + # group again? what if we group groups? + #cat("group predictors\n") + #gp.out.bs <- group.predictors(des.mat, prior, IN$gs.mat, IN$bs.pi, cor.th=0.99, grp.pre='pred.group.bs.') + gp.out.bs <- group.predictors(des.mat, prior, gp.out$gs.mat, IN$bs.pi, cor.th=0.99, grp.pre='pred.group.bs.') + #IN$grouped.pred.bs[[prior.name]][[bootstrap]] <- gp.out.bs + if (length(gp.out.bs$pred.groups) > 0) { + cat('grouped predictors - subsample from prior again\n') + not.done <- TRUE + } + } + + + # set the prior weights matrix + no.pr.weight <- 1 + if (sum(prior != 0) > 0) { + if (PARS$prior.weight == no.pr.weight) { + warning(paste('Priors present, but they will not be used in model selection \ + step, because PARS$prior.weight is set to ', no.pr.weight, '.', sep=''), + immediate. = TRUE) + } + if (PARS$method == 'BBSR') { + no.pr.weight <- 1 / PARS$prior.weight + } + } + #weights.mat <- matrix(no.pr.weight, nrow(IN$exp.mat), length(IN$tf.names)) + #weights.mat[prior != 0] <- PARS$prior.weight + weights.mat <- gp.out.bs$prior.mat * 0 + no.pr.weight + weights.mat[gp.out.bs$prior.mat != 0] <- PARS$prior.weight + + + # set up bootstrap specific design and response + #X <- IN$final_design_matrix[, IN$bs.pi[bootstrap, ]] + X <- gp.out.bs$des.mat[, IN$bs.pi[bootstrap, ]] + Y <- IN$final_response_matrix[, IN$bs.pi[bootstrap, ]] + + if (nrow(X) > 6000) { + #X <- X[IN$tf.names, ] # speeds up MI calculation for large datasets + X <- X[gp.out.bs$tf.names, ] + } + + #if(PARS$use.tfa) { + # X <- IN$tf.activities[[prior.name]][, IN$bs.pi[bootstrap, ]] + #} + + # fill mutual information matrices + cat("Calculating MI\n") + Ms <- mi(t(Y), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores) + diag(Ms) <- 0 + cat("Calculating Background MI\n") + Ms_bg <- mi(t(X), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores) + diag(Ms_bg) <- 0 + + # get CLR matrix + cat("Calculating CLR Matrix\n") + clr.mat = mixedCLR(Ms_bg,Ms) + dimnames(clr.mat) <- list(rownames(Y), rownames(X)) + #clr.mat <- clr.mat[, IN$tf.names] + clr.mat <- clr.mat[, gp.out.bs$tf.names] + + # DREAM8 induced change: + #for (tf1 in IN$tf.names) { + # for (tf2 in IN$tf.names) { + # if (tf1 != tf2) { + # #if (clr.mat[tf1, tf2] > clr.mat[tf2, tf1]) { + # if (Ms[tf1, tf2] > Ms[tf2, tf1]) { + # clr.mat[tf2, tf1] <- min(clr.mat) + # } else if (Ms[tf1, tf2] < Ms[tf2, tf1]) { + # clr.mat[tf1, tf2] <- min(clr.mat) + # } + # } + # } + #} + + # get the sparse ODE models + #X <- X[IN$tf.names, ] + X <- X[gp.out.bs$tf.names, ] + cat('Calculating sparse ODE models\n') + if (PARS$method == 'BBSR') { + #x <- BBSR(X, Y, clr.mat, PARS$max.preds, no.pr.weight, weights.mat, + # prior, PARS$cores) + x <- BBSR(X, Y, clr.mat, PARS$max.preds, no.pr.weight, weights.mat, + gp.out.bs$prior.mat, PARS$cores) + } + if (PARS$method == 'MEN' ) { + stop('MEN currently not tested - remove this line and proceed at own risk') + x <- mclapply(1:nrow(Y), callMEN, Xs=X, Y=Y, + clr.mat=clr.mat, nS=PARS$max.preds, nCv=PARS$enet.nCv, + lambda=PARS$enet.lambda, verbose=PARS$enet.verbose, + plot.it=PARS$enet.plot.it, + plot.file.name=PARS$enet.plot.file.name, + weights.mat=weights.mat, no.pr.val=no.pr.weight, + mc.cores=PARS$cores) + } + cat('\n') + + # our output will be a list holding two matrices: betas and betas.resc + #bs.betas <- Matrix(0, nrow(Y), nrow(X), + # dimnames=list(rownames(Y), rownames(X))) + #bs.betas.resc <- Matrix(0, nrow(Y), nrow(X), + # dimnames=list(rownames(Y), rownames(X))) + bs.betas <- Matrix(0, nrow(Y), length(gp.out$tf.names), + dimnames=list(rownames(Y), gp.out$tf.names)) + bs.betas.resc <- bs.betas + for (res in x) { + bs.betas[res$ind, rownames(X)[res$pp]] <- res$betas + bs.betas.resc[res$ind, rownames(X)[res$pp]] <- res$betas.resc + } + betas[[bootstrap]] <- bs.betas + betas.resc[[bootstrap]] <- bs.betas.resc + + } # end bootstrap for loop + + res.file <- paste(PARS$save.to.dir, "/betas_", prior.name, "_", PARS$prior.weight, ".RData", sep="") + save(betas, betas.resc, file = res.file) + + # rank-combine the rescaled betas (confidence scores) of the bootstraps + confs.file <- sub('/betas_', '/combinedconf_', res.file) + comb.confs <- Matrix(0, nrow(betas.resc[[1]]), ncol(betas.resc[[1]]), + dimnames=dimnames(betas.resc[[1]])) + for (beta.resc in betas.resc) { + comb.confs <- comb.confs + rank(as.matrix(beta.resc), ties.method='average') + } + comb.confs <- Matrix((comb.confs - min(comb.confs)) / (PARS$num.boots * length(comb.confs) - min(comb.confs))) + save(comb.confs, file=confs.file) + + if (PARS$output.summary) { + sum.net(betas, betas.resc, comb.confs, IN, confs.file) + } + +} # end prior.name loop + +PROCTIME <- proc.time() - start.proc.time +save(PARS, IN, SEED, PROCTIME, file = paste(PARS$save.to.dir, "/params_and_input.RData", sep="")) + +# generate network report and visualize TFs and targets +source('R_scripts/net_report_new.R') +Sys.sleep(2) +for (ccf in list.files(PARS$save.to.dir, pattern='combinedconf_*', full.names=TRUE)) { + if (PARS$output.report) { + net.report(normalizePath(ccf)) + } + if (PARS$output.tf.plots) { + vis.tfs.and.targets(normalizePath(ccf), CORES=PARS$cores/2) + } +} + +# this part does not work for grouped predictors +#if (!is.null(IN$gs.mat)) { +# cat('Using gold standard to evaluate results. Evaluate on subset is set to', PARS$eval.on.subset, '. \n') +# summarizeResults(PARS$save.to.dir, PARS$eval.on.subset) +#} + diff --git a/Inferelator/input/.DS_Store b/Inferelator/input/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..307452c8514b0024166ae8db90c7a346882a6ae4 GIT binary patch literal 6148 zcmeHKI|>3p3{BKRv9YwAD|mxJ^aNf&RuKfjVzuAOb9pphK83Q{X(KO?yqQeiEc=Sh zMnrUZ-7iFDA~J>>%FROG?A*L(gUl!pjyo=LloPx??0ePk6ULoLBduH!pZM*rIiF8< zOrru+fC^9nDnJFks6aN?@$`##@+vAo1^z<;`#u!7VNL7;{nLTqBLHxOv>VnwO8|={ zfHkoTL$0iZB|RXQufx~%UQ22@GabG?r<}#or2))80hU78*9gNFN(Ti YYn<1_F3{=7I~~ZM0n>#>1%9o-1Fn=6qyPW_ literal 0 HcmV?d00001 diff --git a/Inferelator/input/bsubtilis/.DS_Store b/Inferelator/input/bsubtilis/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..14735de8559c7a734a2be05389e278d95da43bbc GIT binary patch literal 6148 zcmeHKyK2Kg5ZrZC7`t(q@_rx?rLfz>C|{5tFpfzTP!ZCr@^|IaGW#GPxh8xP&!qn{Cd}6E2C=iZ2p5$5fh^-tZ)wdJI-N*}gtuz1l 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"Heat_1" "Cold_1" "Etha_1" "BMM_2" "Heat_2" "Cold_2" "Etha_2" "BMM_3" "Heat_3" "Cold_3" "Etha_3" "SMM_1" "Salt_1" "SMM_2" "Salt_2" "SMM_3" "Salt_3" "Mt0_1" "M0t45_1" "M0t90_1" "Mt0_2" "M0t45_2" "M0t90_2" "Mt0_3" "M0t45_3" "M0t90_3" "Mt0_1" "M40t45_1" "M40t90_1" "Mt0_2" "M40t45_2" "M40t90_2" "Mt0_3" "M40t45_3" "M40t90_3" "dia0_1" "dia5_1" "dia15_1" "dia0_2" "dia5_2" "dia15_2" "dia0_3" "dia5_3" "dia15_3" "GM-0.2_1" "GM-0.1_1" "GM+5_1" "GM+15_1" "GM+25_1" "GM+45_1" "GM+60_1" "GM+90_1" "GM+120_1" "GM+150_1" "GM-0.2_2" "GM-0.1_2" "GM+5_2" "GM+10_2" "GM+15_2" "GM+25_2" "GM+45_2" "GM+60_2" "GM+90_2" "GM+150_2" "GM-0.2_3" "GM-0.1_3" "GM+5_3" "GM+10_3" "GM+15_3" "GM+25_3" "GM+45_3" "GM+60_3" "GM+90_3" "GM+120_3" "GM+150_3" "MG-0.1_1" "MG+5_1" "MG+10_1" "MG+15_1" "MG+25_1" "MG+45_1" "MG+120_1" "MG+150_1" "MG-0.1_2" "MG-0.2_2" "MG+t5_2" "MG+10_2" "MG+15_2" "MG+25_2" "MG+45_2" "MG+60_2" "MG+90_2" "MG+120_2" "MG-0.1_3" "MG-0.2_3" "MG+5_3" "MG+10_3" "MG+15_3" "MG+25_3" "MG+45_3" "MG+60_3" "MG+90_3" "MG+120_3" "MG+150_3" "LBexp_1" "LBexp_2" "LBexp_3" "LBtran_1" "LBtran_2" "LBtran_3" "LBstat_1" "LBstat_2" "LBstat_3" "LBGexp_1" "LBGexp_2" "LBGexp_3" "LBGtran_2" "LBGtran_3" "LBGstat_1" "LBGstat_2" "LBGstat_3" "BI_1" "BI_2" "BC_1" "BC_2" "BC/t_1" "BC/t_2" "Sw_1" "Sw_2" "BT_1" "BT_2" "B36_1" "B36_2" "B60_1" "B60_2" "M9exp_1" "M9exp_2" "M9exp_3" "M9tran_1" "M9tran_2" "M9tran_3" "M9stat_1" "M9stat_2" "M9stat_3" "M+G_1" "M+G_2" "M+G_3" "Glu_1" "Glu_2" "Glu_3" "Fru_1" "Fru_2" "Fru_3" "Mal_1" "Mal_2" "Mal_3" "G+S_1" "G+S_2" "G+S_3" "Pyr_1" "Pyr_2" "Pyr_3" "Gly_1" "Gly_2" "Gly_3" "Glucon_1" "Glucon_2" "Glucon_3" "aero_1" "aero_2" "aero_3" "nit_1" "nit_2" "nit_3" "ferm_1" "ferm_2" "ferm_3" "HPh_1" "HPh_2" "HPh_3" "LPh_1" "LPh_2" "LPh_3" "LPhT_1" "LPhT_2" "LPhT_3" "SMMPr_1" "SMMPr_2" "SMMPr_3" "HiTm_1" "HiTm_2" "HiTm_3" "LoTm_1" "LoTm_2" "LoTm_3" "HiOs_1" "HiOs_2" "HiOs_3" "Diami_1" "Diami_2" "Diami_3" "Paraq_1" "Paraq_2" "Paraq_3" "H2O2_1" "H2O2_2" "H2O2_3" "Oxctl_1" "Oxctl_2" "Oxctl_3" 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14.2331 14.2505 13.3182 14.2886 13.8362 13.2286 12.8806 12.6183 12.2157 12.0757 14.3259 14.4848 14.2908 13.8502 14.5154 14.6548 11.7472 11.8309 13.6853 14.2665 14.2948 14.4516 14.6505 14.3301 13.8516 14.0062 13.0302 12.2302 15.3251 14.9967 14.7544 13.6141 14.9151 15.0933 14.901 13.901 13.8473 12.866 12.6663 14.4855 14.1513 14.0171 12.9559 13.6162 13.2329 11.5003 11.6185 11.4245 14.4619 14.4877 14.8004 12.8169 13.6761 10.113 10.0522 10.9781 13.9855 14.2291 13.344 14.0215 13.5928 13.8926 15.07 14.7742 13.7292 12.9448 14.433 13.6029 13.8262 13.5103 14.0656 13.7589 13.2476 13.0623 12.7721 13.2224 9.559 11.3872 10.9869 12.3881 12.6065 12.8215 13.8162 13.6636 13.9557 14.6213 13.448 13.222 12.8346 13.0117 13.0448 14.8144 13.9188 14.1013 12.3953 13.1332 12.6722 14.7051 14.9501 13.9006 13.4913 14.29 12.3997 12.4835 11.2133 12.5115 11.8666 11.9448 11.7911 11.4283 11.7321 11.9532 15.2454 15.0841 15.6771 15.2177 15.3145 15.181 14.4293 14.1813 14.2262 13.17 13.1728 13.383 12.9883 13.7307 14.1362 11.6731 11.6024 11.6914 14.8163 14.5943 14.7877 13.8759 14.3024 13.9205 14.2388 14.9383 13.4794 13.9282 14.1418 13.9205 13.8872 13.9184 14.8837 +"BSU08100" 10.1872 11.7948 10.8903 10.9375 12.0351 12.3 12.58725 10.3466 11.9941 11.0697 11.1323 12.1678 12.997 11.506 10.657 12.1694 10.6316 11.4999 11.3942 12.5055 12.3297 11.382 12.1262 11.5114 12.1651 11.9436 11.4301 12.1441 12.0569 8.755 10.7367 8.073 9.5529 8.389 7.8211 11.3143 7.9251 8.6686 8.5384 9.1321 8.3456 9.0804 8.8926 8.995 8.9553 9.1006 9.2602 8.9627 10.9335 12.1857 12.0909 12.1951 12.1275 12.7861 12.263 12.5399 12.2912 11.1911 8.0109 9.9074 7.6307 8.0548 7.8007 9.7172 7.4478 7.9995 8.0379 9.608 7.3916 7.8538 8.5301 8.1822 8.5395 8.0863 8.4973 8.2216 9.9058 9.1705 12.0956 12.1442 8.6382 13.2544 11.9325 9.7278 12.7676 9.9058 9.7915 12.7109 12.1442 13.1894 13.394 11.9325 9.9486 13.0573 10.5328 11.1489 11.9204 10.5886 11.8431 10.3719 9.7125 11.9165 9.3132 8.9539 8.6552 8.2969 8.2827 8.2481 8.1885 7.8621 8.1343 8.5606 8.3184 9.0993 9.0272 8.7724 8.1372 8.056 7.9924 7.268 7.6979 8.4225 8.0564 8.7249 8.8651 8.9371 7.8688 8.0954 7.9752 8.3201 7.9622 8.1256 8.5899 8.1497 9.759 8.271 8.2071 7.6354 7.8171 7.8931 8.8234 8.8258 9.0723 9.5327 7.9592 7.8388 8.5402 7.6648 7.8021 8.1281 7.7456 8.919 10.3849 9.9066 8.2192 8.0854 8.0654 8.0998 8.4417 8.2665 8.3642 8.5366 8.6506 10.321 9.7791 9.1359 13.1707 11.7574 11.2993 12.44 11.9985 12.0019 8.1014 7.9438 8.1333 7.9272 8.1874 7.4084 7.4224 7.9753 12.1905 12.2463 12.9082 13.3665 13.1629 13.2695 13.127 13.3655 8.5682 8.0303 8.734 8.2676 11.3006 11.0077 8.3321 8.1145 8.0294 9.1657 8.7132 9.1927 11.4203 11.511 11.3957 8.7984 8.0866 7.9384 9.0183 8.9214 8.8338 9.1968 9.7373 9.0055 9.478 9.7416 10.3534 9.175 10.9333 11.1829 10.0579 10.1225 9.9873 8.8796 9.3673 8.75 9.5334 10.0227 9.6688 8.6329 7.6889 8.2417 8.826 8.9821 8.6863 8.6714 8.4616 9.052 8.2516 7.7067 8.0563 8.1097 8.2091 8.0528 8.0817 8.0327 8.3999 8.7622 8.9126 8.9562 9.4305 10.2492 9.4722 7.9255 7.6377 7.5817 9.8392 9.5114 9.778 10.3101 10.3288 10.5813 9.3153 9.6074 8.0938 9.5922 9.9515 9.025 9.325 9.6 9.4028 +"BSU01810" 7.7582 8.7416 8.6554 9.9802 10.667 10.2734 9.97555 7.8621 8.5261 8.4782 10.0531 10.2114 10.8392 10.0639666666667 7.8442 8.6078 8.2601 10.3841 10.0089 10.6882 10.2271 9.5367 10.2319 9.9286 9.7912 10.2286 10.0629 9.9743 10.2363 8.4806 8.3828 8.7008 8.8471 8.788 7.8955 8.7198 8.3761 9.1631 8.7929 8.9797 8.2741 7.931 7.7301 7.6958 7.7289 7.6483 7.7045 7.5218 7.5067 8.5744 8.0464 8.4071 8.3759 8.4342 8.2357 8.4766 8.6 8.5777 7.855 8.9425 7.7278 8.2096 7.8661 8.5809 7.7171 8.3456 7.9374 8.8425 7.6983 8.0811 7.8294 7.4273 7.6737 7.3493 7.68 7.3835 8.1768 8.2195 8.2793 8.6861 8.1221 8.6677 8.3317 8.5434 8.6564 8.1768 8.2619 8.366 8.6861 9.3811 8.8105 8.3317 8.6379 9.2504 8.0453 8.7193 8.1787 8.2693 8.5341 8.4677 8.025 8.1951 8.0564 7.9654 7.6734 7.5102 7.693 7.9205 7.7805 7.6871 7.7503 7.8642 7.974 8.4529 7.9802 7.4579 7.6013 7.9577 8.0124 7.1416 7.4827 8.2785 7.235 7.8924 8.1749 7.7742 7.3677 7.8016 7.9571 8.0992 7.8758 8.1926 8.5804 7.9705 7.83 8.012 8.0285 7.7335 7.8226 7.8812 7.7214 7.9943 7.2352 7.5413 7.4989 7.6355 8.6178 7.5526 7.3013 7.7697 7.1968 7.9415 8.2059 8.2764 8.2472 7.9049 8.1176 8.0362 8.0816 7.9637 7.9188 7.8482 7.9341 8.3227 8.2466 8.2078 9.108 8.3782 8.3249 8.183 8.0729 7.9554 7.9709 8.1445 8.1342 8.4575 8.6453 7.9273 7.8988 8.4532 8.2395 8.1459 10.0988 11.0311 9.8246 10.8861 9.756 10.3103 8.6317 8.268 8.5651 8.475 8.5823 8.8544 8.0411 7.9669 7.724 7.9062 8.0417 7.948 8.5708 8.5147 8.2197 8.8526 7.8055 7.8472 8.2868 8.105 8.0407 8.0306 8.2634 7.9937 7.6009 8.0195 7.9413 8.105 8.6049 8.8445 8.6788 8.6067 8.6311 7.9347 8.1897 8.1942 8.4388 8.3983 8.353 8.0111 7.6833 7.9867 8.1503 7.9535 8.1453 8.266 7.8653 8.3448 8.0815 7.7162 8.0674 7.9751 7.9218 8.0996 8.531 8.2822 8.5143 7.6208 7.8616 7.9631 8.4115 8.2701 8.4727 8.6036 8.5393 8.577 8.1725 8.2017 8.1973 8.0915 8.3239 8.4552 8.418 8.3456 7.6488 8.015 8.0968 8.017 8.7287 8.1713 8.1778 +"BSU24250" 11.0569 11.082 10.7409 11.136 11.4502 10.9955 10.26385 11.2647 10.9183 10.6686 11.3374 10.9111 11.2159 9.63286666666667 11.0246 11.3857 10.4692 10.852 10.796 11.4169 10.3147 9.9225 10.4378 9.76 10.4948 10.2391 9.6712 10.5444 10.397 12.6802 11.9272 12.391 12.6917 12.9619 10.692 12.5275 11.3013 11.1443 11.0417 11.3361 11.4518 12.0816 11.7956 11.6258 11.4187 11.2895 11.5519 11.3183 11.1014 10.2674 10.1937 10.2309 10.2433 11.035 10.6909 11.1008 11.071 10.6287 11.8768 12.8625 12.3287 10.7607 11.6635 12.4 11.2922 10.345 12.0816 11.8771 10.7201 10.7416 11.5326 12.0256 11.4175 11.8361 11.4831 11.9635 11.6866 10.8441 10.1688 10.1436 11.745 11.2927 12.1911 11.4314 10.8337 11.6866 10.8034 10.2876 10.1436 11.0314 10.9574 12.1911 11.5995 10.9459 11.5319 10.9068 12.5003 11.8184 11.2226 12.9587 12.1953 11.2344 11.8723 11.318 11.4268 11.0786 11.7409 11.9682 11.914 11.642 11.9161 10.7933 11.193 11.8958 11.7311 11.5792 11.4715 11.3956 11.4943 10.9341 10.785 11.6057 10.7049 11.4737 11.532 11.6063 10.4911 11.2884 10.9492 11.0439 10.7189 11.5193 11.1254 11.6182 11.7042 11.6221 11.9532 11.1494 11.6322 11.5005 10.6975 11.4685 10.7765 11.2866 11.213 11.5194 12.3136 11.5703 10.9588 11.2736 11.0912 11.3451 12.1124 11.3558 11.0221 11.0373 11.8129 11.8166 12.1524 11.3793 11.6805 11.1315 11.4188 12.2089 11.6336 11.3407 11.1495 10.5491 10.6316 10.8931 10.5421 10.2258 12.2388 12.1913 12.0207 10.9219 11.7038 10.8396 10.623 10.7183 11.5951 11.7851 11.3098 12.0452 11.1999 11.7632 11.292 12.0995 9.8125 9.3651 11.05 10.7989 11.1664 11.154 12 11.6022 11.3148 11.1304 11.0574 11.3532 9.7096 9.5944 9.547 10.9527 11.3743 11.0648 11.8962 11.8964 11.7493 11.5945 11.6079 10.9594 10.5995 11.0278 11.3075 11.8307 10.809 11.1145 10.601 11.0792 10.9129 10.1189 10.6721 9.331 10.7334 11.212 10.5031 12.1485 10.8428 11.9552 11.2739 11.3197 11.2221 11.0898 11.0512 11.647 11.9186 11.5118 11.4991 11.9157 12.4683 11.8076 10.9814 10.952 10.8516 11.0254 11.1618 11.2475 11.1025 11.6242 11.5949 12.0776 11.6951 11.7113 11.6977 11.4657 11.5876 10.8917 11.4116 11.3908 12.3221 12.7902 11.3578 12.9568 13.1831 12.9119 11.7903 11.804 12.6147 +"BSU36020" 9.7198 9.8195 10.1027 9.8789 9.5048 9.169 8.6941 9.5583 9.8887 10.1614 9.8242 9.5444 9.1537 8.13 9.8092 9.8982 10.5487 9.3686 9.2928 9.4235 8.9614 8.7432 9.2262 9.1984 9.0626 9.2546 9.1065 9.066 9.6391 9.7848 9.2579 9.5978 10.0226 10.0714 8.5254 9.2408 9.1843 9.166 9.99 9.4366 10.0918 10.5551 10.5513 10.5514 10.3093 10.3041 10.5118 10.4238 10.0592 9.7441 9.4011 9.3515 9.1335 9.6474 9.3154 9.6132 9.5891 9.2227 10.2104 12.0168 9.6516 11.9427 10.0837 11.7322 9.1919 11.5294 10.8225 11.5725 9.1601 11.9069 10.3056 10.987 10.199 10.6196 10.3311 10.8549 10.3881 11.3236 9.5508 9.5816 10.7977 10.8621 9.8827 11.6823 10.4695 10.3881 10.7163 9.6542 9.5816 10.2106 10.8484 9.8827 11.9068 10.8542 10.0377 11.7313 12.1303 10.174 11.5184 11.5145 10.2849 11.3688 11.0292 9.8922 9.9856 9.9518 10.2912 10.6979 10.575 10.4059 10.3047 9.4739 10.196 10.1638 10.5902 10.3962 10.2832 10.2192 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7.7037 7.0995 7.1634 7.834 11.9981 11.968 10.1699 10.943 10.4156 10.8653 9.3987 9.5329 11.6945 11.3507 10.3291 10.1045 9.4827 9.0638 10.3408 10.0755 9.4126 11.2476 11.0269 11.3721 9.177 8.6944 8.5 9.9317 9.6705 9.6878 10.022 10.0629 10.3888 9.0517 11.2606 10.0085 9.8155 9.6194 9.8657 9.0359 10.7809 11.0684 9.928 10.7491 10.4234 9.0055 9.9598 8.7606 9.8061 10.1253 9.3621 8.6809 8.6786 8.7732 10.9312 11.6092 11.3819 10.3701 10.5737 11.1867 8.293 8.0503 8.2328 8.0402 8.4079 8.2904 9.3259 9.1989 9.5354 10.5507 10.5579 10.3509 8.1674 7.6201 8.0099 9.0254 8.7918 8.743 11.3261 11.2675 11.4314 7.4613 7.0902 7.6253 7.304 6.977 7.045 7.0424 7.1733 7.4795 7.7098 7.5641 7.0958 +"BSU17170" 9.0805 12.4283 12.1906 11.7224 11.1974 10.6926 8.62125 8.9091 12.1284 12.5102 11.8197 10.9974 10.6966 7.9867 8.8452 12.3607 11.737 10.3492 10.4222 10.9331 9.5341 9.0462 9.3621 8.9525 9.5565 9.0995 8.5762 9.4956 9.4737 7.3153 7.2126 7.2067 6.8938 6.9346 7.4253 7.2369 6.9306 9.2078 8.823 9.4603 8.9124 11.7769 11.5864 11.2651 11.0988 10.8306 10.8721 10.2778 10.5348 8.1831 9.634 9.1651 9.2998 9.9449 9.4695 9.6206 9.5316 9.2597 9.3371 7.7188 7.8604 8.0665 9.0391 7.4079 7.5852 8.1164 8.9829 7.7088 7.6251 8.0374 10.13 8.1608 10.1388 7.9319 10.0376 8.0136 7.2696 8.3355 10.291 7.8468 7.1743 11.0232 6.7702 8.4915 11.2123 7.2696 8.3612 10.6272 7.8468 8.0288 10.8288 6.7702 8.3234 11.2016 7.0786 7.3458 7.319 7.0328 7.3741 7.1866 7.027 7.0401 7.2202 9.9889 9.9485 9.6299 9.9437 10.0102 10.1645 10.1062 10.1243 9.3712 9.9076 9.5894 9.3788 9.2505 8.9352 8.9516 8.8647 8.1562 8.4393 8.7085 10.6911 9.7116 9.7732 9.9321 8.7991 9.4557 9.448 9.4612 9.4671 9.6854 9.2988 9.9512 8.9298 8.9648 9.3346 8.4468 8.7285 8.5818 9.7176 10.5988 8.1201 8.645 8.6706 8.9379 9.5277 8.5504 8.3172 8.6448 8.5538 10.9478 8.9184 8.5079 8.5337 8.4579 8.4576 8.6951 8.8996 8.487 8.9503 10.4196 10.465 7.0488 7.0964 7.1966 7.9934 8.2451 8.6143 11.1906 11.0493 10.7516 6.9831 6.9615 7.1177 7.7462 7.7781 6.9961 7.1702 7.7833 11.7404 11.7141 10.0208 10.6295 9.9231 10.5529 8.5899 8.7982 11.2044 11.0343 9.9335 9.6296 9.0547 8.926 10.3527 9.9585 9.4952 11.2023 11.1458 11.2336 9.1121 8.6344 8.3808 9.9905 9.8239 9.7993 10.1516 9.9993 10.4455 9.0802 11.4139 10.1911 9.7199 9.652 9.7642 9.0563 10.6384 11.0844 9.7892 10.7073 10.4838 8.6386 9.579 8.5729 9.7034 10.0025 9.2221 8.6443 8.4717 8.7901 10.902 11.5541 11.4553 10.5139 10.7547 11.2668 8.4082 8.1425 8.3533 7.9772 8.2599 8.2789 9.1649 9.0775 9.2517 10.4086 10.4075 10.4234 7.9584 7.6472 7.9779 8.9926 8.6982 8.5727 11.3586 11.2603 11.4487 7.4508 7.0591 7.6267 7.3136 6.9239 7.0351 6.9969 7.179 7.3977 7.7036 7.5295 7.1355 +"BSU17180" 9.6318 12.3602 12.1919 11.6863 11.1954 10.6645 9.1059 9.4779 12.1121 12.703 11.8325 11.2283 10.7396 8.1399 9.5179 12.4316 12.0606 11.3108 10.6802 10.9427 9.9048 9.3091 9.4761 8.9612 9.8787 9.1774 8.5351 9.7858 9.5443 7.4791 7.4286 7.3319 7.2119 7.1985 7.4356 7.4679 7.0324 9.2255 8.6353 9.5471 8.8902 11.8326 11.5792 11.4494 11.2748 10.9462 11.0787 10.472 10.599 9.2867 10.1933 9.6156 9.6166 10.4176 9.8099 9.8973 9.7535 9.4236 9.3722 8.1157 9.3147 7.9812 9.0711 7.9367 8.4277 7.9942 9.0186 8.123 8.0989 7.871 10.1456 8.975 10.2322 8.7789 10.1088 8.9909 7.4188 8.3646 10.5567 8.2124 7.5528 11.4136 7.094 8.5022 11.411 7.4188 8.4371 10.8946 8.2124 8.7324 11.3347 7.094 8.3696 11.3281 7.3276 7.3579 7.9372 7.3828 7.5241 7.9742 7.3846 7.1652 7.5944 10.1953 10.0191 9.8127 9.9559 9.9815 10.1889 10.1678 10.3848 9.486 9.9632 9.6229 9.3787 9.4671 9.2444 8.9268 9.0163 8.3641 8.6412 8.9573 10.6962 9.7918 9.8656 10.2537 9.0416 9.5166 9.381 9.4729 9.5037 9.7391 9.3537 9.8057 9.0548 9.0767 9.4772 8.6151 8.9228 8.7491 9.9155 10.577 8.3244 8.7299 8.7572 9.2022 9.5821 8.7507 8.4894 8.8563 8.7008 10.9972 8.9939 8.5878 8.4863 8.6991 8.772 8.8444 9.0945 8.5786 9.0388 10.4037 10.4565 7.3942 7.3004 7.408 8.2816 8.3176 8.5286 11.484 11.6387 11.4387 7.2112 7.2805 7.3482 7.8482 7.9309 7.591 7.5826 7.9899 12.2956 12.4793 10.7254 11.3655 10.8121 11.2394 10.3137 10.9771 11.9942 11.5932 10.4956 10.2483 9.3517 8.8237 10.5209 10.1209 9.5644 11.4143 11.2054 11.4096 9.0824 8.6285 8.4287 10.072 9.8273 9.782 10.0787 10.0189 10.4576 9.0963 11.299 10.2118 10.0467 9.682 10.0031 9.1267 10.6962 11.0673 9.8541 10.5661 10.3157 9.7569 10.5079 9.2048 9.7572 9.8006 9.3077 8.6972 8.5165 8.8539 10.8229 11.4566 11.1967 10.3518 10.5264 10.9319 8.4126 8.1196 8.3085 8.1553 8.4398 8.3992 8.9913 8.8751 9.1334 10.4755 10.6356 10.4615 8.6091 8.1936 8.3142 8.9676 8.8359 8.7967 11.4574 11.1886 11.4185 7.7334 7.5428 7.9772 7.7062 7.4334 7.2477 7.3921 7.6279 7.7549 7.854 7.7298 7.4164 +"BSU17190" 10.0627 11.9956 12.4901 11.9048 11.4996 10.9276 9.4373 9.8795 11.9391 12.9231 12.0985 11.7205 11.1168 8.324 10.0594 12.3036 12.391 11.7626 11.0729 11.2485 10.2712 9.5669 9.6099 9.0775 10.166 9.2378 8.58 10.0531 9.6831 7.8766 7.8038 7.6713 7.7567 7.7373 7.5829 7.9461 7.2967 9.3697 8.7125 9.6686 9.0185 12.2694 11.9978 11.8802 11.7524 11.4049 11.493 10.938 11.0565 9.3762 10.4745 9.7765 9.796 10.6793 10.0851 10.1378 9.8967 9.5941 9.9696 9.1653 10.7126 8.8015 9.5067 9.0998 9.7022 8.5025 9.5524 9.0182 9.1985 8.7023 10.5332 10.4871 10.6193 10.2653 10.5001 10.4534 7.8973 8.7862 10.8308 8.5422 8.1683 12.053 7.7613 8.929 11.5633 7.8973 8.8246 11.2379 8.5422 9.2482 11.8526 7.7613 9.0393 11.2438 7.8448 7.856 8.5136 7.915 7.9395 8.7638 8.0581 7.6893 8.2123 10.6496 10.4147 10.2786 10.425 10.5499 10.6623 10.64 10.8633 9.9677 10.2747 10.0985 9.8847 9.943 9.8421 9.3677 9.4284 8.8277 9.1406 9.4398 10.9596 10.2108 10.1628 10.6932 9.5467 10.0198 9.7287 9.8153 9.8845 10.1993 9.8721 10.2108 9.5671 9.5713 9.9069 9.1498 9.3993 9.3193 10.1857 11.1459 8.8297 9.2329 9.2694 9.6508 10.2648 9.3044 8.9928 9.3403 9.1282 11.257 9.576 9.0752 8.9289 9.2224 9.4755 9.3177 9.728 9.1417 9.4633 10.6875 10.9013 7.9152 7.8627 7.8868 8.6881 8.5891 8.7618 11.6105 11.7591 11.5649 7.7585 7.8333 7.837 8.3557 8.539 7.6965 7.7171 8.3403 12.3335 12.6016 10.3765 11.0424 10.376 10.9111 9.9215 10.6055 12.3643 12.0349 10.6152 10.4216 9.4925 8.931 11.1262 10.7197 10.0889 11.8954 11.7754 11.9132 9.2714 8.6874 8.4744 10.4737 10.2219 10.0955 10.4919 10.3704 10.8955 9.5117 11.6532 10.6023 10.5063 9.9938 10.4104 9.6431 11.0366 11.4574 10.0702 10.7735 10.5577 10.2765 11.0006 9.5569 10.0892 10.1342 9.6924 8.992 8.7438 9.2795 11.0035 11.7082 11.5105 10.6302 10.8706 11.2597 8.7832 8.4464 8.6142 8.6603 9.0046 8.8133 8.8791 8.8428 9.0301 10.8599 11.1021 10.8759 9.2812 8.8023 8.875 9.4817 9.411 9.3449 11.5636 11.2738 11.5956 8.1804 8.1237 8.4719 8.1655 8.0763 7.505 7.7924 8.0705 8.1056 8.2013 8.0556 7.9363 +"BSU17200" 9.926 10.5043 12.061 11.3761 10.8813 10.2756 8.7584 9.7471 10.8436 12.3301 11.4756 11.0749 10.3301 8.0488 9.8409 11.1531 11.6452 10.9019 10.4538 10.6047 9.5753 9.0994 9.3605 8.8539 9.5778 8.9942 8.6799 9.5015 9.4288 8.1133 7.9715 7.8897 8.0598 8.0165 7.6226 8.1944 7.4389 9.005 8.5037 9.3055 8.7019 11.7519 11.546 11.5336 11.3937 11.0178 11.1719 10.5754 10.6483 9.1729 9.9236 9.1537 9.1942 9.844 9.2503 9.3208 9.0425 8.8366 9.7654 9.4816 10.2417 8.7787 9.3537 9.3273 9.3317 8.6067 9.4034 9.3081 8.9814 8.7806 10.1733 10.0349 10.2599 9.8057 10.0864 10.0248 8.0174 8.8089 10.0802 8.4263 8.3864 11.593 8.0218 8.8933 10.3439 8.0174 8.8435 10.5842 8.4263 9.0832 11.3608 8.0218 9.0286 9.8164 8.0281 8.0092 8.5634 8.1213 8.018 8.9087 8.2737 7.7205 8.3048 10.2151 9.9954 9.9445 10.0834 10.2055 10.3349 10.3561 10.5152 9.7238 10.0456 9.7681 9.4779 9.5521 9.4806 9.1387 9.1462 8.5668 8.8966 9.3877 10.6453 9.6985 9.641 10.2149 9.2506 9.6568 9.3713 9.5219 9.5945 9.8952 9.7828 9.8951 9.3664 9.3371 9.6895 8.9597 9.2477 9.1363 9.7564 10.7634 8.6421 9.034 9.1374 9.4812 9.9996 9.0747 8.7968 9.2228 8.9019 10.6892 9.4133 8.9356 8.8586 8.9258 9.235 9.2182 9.6496 9.0185 9.2957 10.3167 10.5836 8.1205 8.0869 8.0519 8.3666 8.426 8.647 10.8559 10.9588 10.6857 7.9135 8.0094 8.0907 8.4 8.5574 7.6159 7.5807 8.3252 11.7294 11.9733 9.1491 9.6492 9.0929 9.6999 9.1089 9.7132 12.2329 11.8324 10.3617 10.2151 9.1088 8.5827 10.742 10.339 9.7746 11.4791 11.3439 11.4453 8.8213 8.3771 8.2055 10.1804 9.946 9.83 10.1166 10.0441 10.5941 9.3457 11.2987 10.2233 10.1698 9.7589 10.1403 9.415 10.7349 11.0553 9.4887 10.3456 10.0977 9.8091 10.4801 9.2581 9.7642 9.8176 9.4069 8.79 8.5904 9.1846 10.7215 11.3902 11.1128 10.307 10.5621 10.9386 8.7956 8.3811 8.6016 8.6152 8.9702 8.787 8.4075 8.2222 8.4725 10.5549 10.8006 10.5022 9.2029 8.7603 8.8056 9.0705 8.927 8.9033 10.974 10.7542 11.0028 8.0952 8.0574 8.3703 8.2502 8.2527 7.6507 7.9855 8.2481 8.1998 8.2824 8.16 8.1473 +"BSU17210" 9.7339 10.3758 12.3508 11.6098 11.0689 10.6486 9.5414 9.5282 10.4225 12.4992 11.5656 11.3231 10.8515 8.29963333333333 9.7949 10.9306 12.0309 11.1388 10.81 10.9697 10.2028 9.4231 9.6126 9.166 9.9934 9.3412 8.7282 9.8948 9.6699 7.6718 7.6296 7.5283 7.512 7.527 7.504 7.7781 7.1514 9.1167 9.1181 9.3332 9.4049 11.9234 11.7464 11.6997 11.5552 11.3003 11.4896 10.9017 10.9929 10.3592 10.3359 10.1326 9.9177 10.7488 10.1292 10.2947 10.1482 9.6188 10.0837 9.9854 10.3073 9.1567 9.6905 9.9189 9.3286 8.8294 9.6528 9.7886 8.9619 9.0832 10.3302 10.0876 10.3986 9.8743 10.2613 10.1355 8.0628 9.0114 10.2218 9.1118 8.3217 11.8004 7.7703 8.969 10.7737 8.0628 9.017 10.54 9.1118 9.8466 11.3841 7.7703 8.9317 10.5612 7.8215 7.8943 8.3392 7.9681 8.0145 8.4239 8.2162 7.779 7.9489 10.4869 10.278 10.218 10.4588 10.4842 10.5613 10.5528 10.6502 9.8408 10.1151 10.1677 9.9973 9.9961 9.8494 9.5863 9.4142 8.9497 9.2193 9.68 10.6366 10.0895 10.0248 10.4949 9.4372 9.9927 9.6453 9.7322 9.783 10.1145 9.9457 10.0245 9.824 9.6958 10.2106 9.343 9.7297 9.4642 9.8822 10.853 9.0247 9.5105 9.555 9.93 10.3584 9.5016 9.15 9.6336 9.1929 10.6661 10.0011 9.4199 9.2098 9.4064 9.7482 9.772 10.1256 9.3966 9.7718 10.4479 10.6822 7.8667 7.9481 7.943 9.2053 8.9908 9.093 11.2257 11.2348 11.1241 7.4873 7.6525 7.6167 8.5435 8.686 7.9264 7.943 8.3884 11.7882 12.0487 10.6677 11.2216 10.689 11.109 10.3442 11.0303 12.2819 11.8547 10.6127 10.4562 9.777 9.277 10.9535 10.5766 9.9492 11.5105 11.423 11.4847 9.3305 8.8837 8.7162 10.3824 10.2031 9.9928 10.4737 10.3902 10.848 9.8785 11.4759 10.4483 10.3233 9.9562 10.452 10.0397 11.0959 11.292 9.8442 10.606 10.3736 10.2646 10.9291 9.5916 10.0311 10.1367 9.6974 9.052 8.8536 9.4522 10.7302 11.3948 11.1315 10.4818 10.6019 10.9794 8.9809 8.6336 8.7586 8.8802 9.2783 8.9532 8.9452 8.8622 9.2115 10.9492 11.154 10.8943 10.6742 9.9368 10.1997 9.2822 9.0724 9.1158 11.2332 10.9387 11.2317 7.9916 7.9074 8.2156 7.9349 7.8398 7.4311 7.5359 7.7092 7.8114 8.0609 7.9446 7.733 +"BSU17220" 10.0098 10.3692 12.676 11.914 11.5182 11.1064 10.36605 9.7918 10.2601 12.666 11.6715 11.7429 11.2663 8.86163333333333 10.0865 10.6725 12.2782 11.54 11.2236 11.4197 10.8827 9.8692 9.988 9.6161 10.3974 9.863 8.9265 10.352 10.0038 8.2336 8.1163 8.0634 8.1959 8.244 7.7141 8.4246 7.5814 9.4617 9.2141 9.725 9.5042 12.113 11.9026 11.8656 11.7245 11.5021 11.6722 11.1767 11.1913 10.9918 10.5222 10.6869 10.3697 11.2406 10.6389 10.8315 10.6425 10.0636 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13.8071 13.9189 13.6552 12.6292 13.554 13.0614 13.9995 10.9462 13.3283 12.4901 13.4945 11.0906 13.102 12.6164 12.8497 11.2283 14.0507 12.0118 14.1037 11.9735 14.0813 12.0354 12.0107 12.9874 13.099 13.1547 12.8487 14.0418 11.8418 13.1683 13.7512 12.0107 12.8237 13.2456 13.1547 14.3456 14.0343 11.8418 12.9638 14.015 12.0947 12.2664 10.5496 12.6551 12.1458 10.9241 12.8096 12.0442 11.6899 12.958 13.0824 13.1731 13.2628 14.1413 13.9023 13.8521 13.9699 12.8708 13.9661 13.6238 13.1804 12.977 12.899 12.9254 13.4962 12.3768 12.76 14.0418 13.2917 13.8449 13.9423 13.709 12.5109 13.5592 13.3171 12.7399 12.9743 14.1803 14.0075 14.0812 12.9036 12.7508 13.507 12.8148 13.3051 13.0952 12.7549 14.1821 11.8861 12.3407 12.4213 12.8461 13.7782 12.7002 11.9248 12.769 12.5896 14.2016 12.971 12.5405 12.5034 11.388 12.903 12.986 13.0035 12.1687 13.4035 13.6664 14.132 11.62 12.3878 12.3355 13.8454 13.4787 13.649 13.3627 13.1841 13.0702 11.3822 11.8868 11.5972 12.4772 12.673 11.5647 11.8545 11.9828 13.6954 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10.86 10.5814 11.8169 11.3888 11.1182 10.9936 11.1165 11.2276 9.4226 8.8763 8.6135 10.9585 11.2346 11.1709 11.4147 11.3681 11.5364 11.6622 11.5525 10.9495 11.1217 10.8646 11.1591 11.6262 10.9778 11.2777 10.6936 10.9286 10.8046 10.9566 11.6738 9.9409 10.407 10.7793 10.2139 12.2441 11.2273 12.1523 11.2642 11.4055 11.0638 10.7774 10.8791 11.5586 11.6566 11.4776 11.2121 11.7459 12.307 11.8034 11.6108 11.6814 12.031 11.2743 11.4144 11.4116 11.4165 11.3704 11.242 11.2404 10.9425 11.0119 11.4212 11.1107 11.2666 13.8275 14.1867 14.0004 12.6591 12.8068 11.2074 13.4532 13.758 13.5725 11.4951 11.6909 12.3833 +"BSU29560" 10.4699 11.232 10.7496 11.6653 12.1399 11.4778 11.10665 11.1048 11.0748 10.6739 12.0393 11.8402 11.7328 9.65633333333333 10.9417 11.6182 10.227 12.0171 11.6144 11.8258 11.1355 10.6964 10.8745 10.3101 11.3009 10.7698 10.0836 11.4288 11.1196 12.0958 11.897 11.7799 12.2843 12.3121 10.1373 12.3267 10.9408 10.4706 11.7089 10.6061 12.1265 12.026 11.8874 11.6684 11.5759 11.5952 11.6617 11.6301 11.4901 9.6413 9.7495 9.5373 9.7714 10.7789 10.3076 10.8421 10.9389 10.4852 11.8377 11.9018 11.4994 11.4856 11.7327 11.7736 10.8147 10.9322 11.5725 11.4887 10.3186 11.6683 11.3807 12.1389 11.3709 12.1301 11.4444 12.2705 12.3167 11.8655 10.3275 10.0499 12.0241 10.9474 12.0188 12.0306 10.993 12.3167 11.7239 10.6207 10.0499 10.8553 11.2143 12.0188 12.0867 11.3942 11.8743 13.4778 14.8393 11.8592 13.7441 14.6872 12.1783 13.8793 13.9651 11.4131 11.3373 11.0585 11.6647 11.8872 11.7523 11.6692 11.8202 10.8178 11.213 11.5604 11.5269 11.4559 11.4425 11.2933 11.4219 10.5677 10.471 11.5304 10.5387 11.3284 11.348 11.5419 10.4058 11.2594 10.7893 10.6274 10.4943 11.1406 11.2561 11.2815 11.5441 11.3928 11.9617 11.0326 11.5666 11.2923 10.5925 11.0304 10.591 11.1684 11.3585 11.7048 12.2089 11.3612 11.1356 11.5726 11.2384 11.523 12.0514 11.2508 11.1718 10.8217 11.7898 11.6975 12.1695 11.6894 11.6006 11.0734 10.9257 12.4596 12.0595 11.6603 10.853 10.8444 11.5063 10.7536 10.4308 10.4645 12.3573 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12.2272 11.884 11.9586 12.3137 12.4072 10.4686 12.3392 11.2576 11.5814 11.8821 11.8216 12.2129 14.9727 14.3013 12.9798 11.8145 11.3028 11.3404 11.6933 11.3401 11.1796 10.4246 10.3132 9.9343 10.5069 10.2672 11.6603 13.6767 15.7134 11.3877 12.7828 11.5314 10.3736 11.0484 12.4901 11.3318 9.966 11.7781 12.3671 11.1056 9.7725 11.4372 11.4515 11.4322 11.634 11.8406 10.8963 12.3892 12.0573 10.4239 10.8558 12.4169 11.3999 12.4141 12.4758 11.0257 12.3892 11.7258 10.6938 10.8558 11.9333 11.4892 12.4141 12.2734 11.5564 12.1613 11.661 14.9009 12.4391 12.8742 14.8731 12.5932 12.824 13.1515 11.2399 11.5912 11.2365 11.7267 11.7286 11.7561 11.571 11.5845 10.6587 11.0328 11.2523 11.3812 11.2638 11.3664 11.4512 11.3863 10.7755 10.6955 11.4844 10.1699 11.2519 11.3122 11.2466 10.436 11.2442 10.8486 10.8531 10.6935 10.851 11.2806 11.1483 14.1267 13.6339 13.9411 13.3085 13.4083 12.7453 11.0766 10.6098 11.4624 12.2016 12.1821 12.2092 12.1225 11.6489 11.2108 11.5417 11.1035 11.0699 11.9152 11.5628 11.2955 11.0055 11.6458 11.6765 12.0493 11.437 11.3614 10.6518 10.8853 12.3389 12.0698 11.7687 11.4304 11.2543 11.5086 11.0777 11.0405 10.6541 11.7812 11.7811 11.9914 11.7325 12.3039 11.9272 11.6796 11.3282 13.6986 14.1873 13.6057 14.5853 13.9161 14.7387 11.4221 11.8699 16.387 16.3299 16.3776 16.212 16.1071 15.3421 12.5507 12.2356 11.0851 10.6126 10.8574 10.9471 12.835 9.2758 9.1891 11.4768 11.6285 12.2437 11.414 11.0982 11.6684 11.1474 11.4456 11.1307 10.6854 10.9811 11.1839 11.1872 16.3771 16.3685 16.2574 16.3697 16.3791 13.0441 13.5036 11.7951 14.4676 14.8566 10.1343 14.4121 12.334 14.2184 16.2874 16.3604 16.3535 16.3134 16.3326 16.3524 12.7744 12.2312 12.3842 12.4743 12.7388 12.2052 13.2408 12.9583 13.1355 15.2281 14.7556 14.5723 12.7824 11.7747 12.2469 14.6454 14.9213 15.4719 12.7196 13.1373 12.628 13.4816 13.8778 13.8376 12.6146 12.7846 11.7492 13.3346 13.4127 13.4893 12.0624 12.0668 12.5411 +"BSU29580" 11.1942 11.1659 10.2087 11.1125 11.8126 11.8074 11.36765 11.473 11.2101 10.0037 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+"BSU04160" +"BSU01690" +"BSU27890" +"BSU35030" +"BSU02740" +"BSU09380" +"BSU10560" +"BSU13150" +"BSU08340" +"BSU32140" +"BSU08730" +"BSU29110" +"BSU17080" +"BSU34720" +"BSU32420" +"BSU00470" +"BSU03230" +"BSU39990" +"BSU35910" +"BSU15670" +"BSU23120" +"BSU05970" +"BSU33660" +"BSU40350" +"BSU14240" +"BSU37620" +"BSU01540" +"BSU09990" +"BSU33790" +"BSU08810" +"BSU24610" +"BSU26350" +"BSU34380" +"BSU13920" +"BSU24220" +"BSU36420" +"BSU00560" +"BSU11500" +"BSU13310" +"BSU07820" +"BSU40410" +"BSU12510" +"BSU17590" +"BSU02220" +"BSU02320" +"BSU02370" +"BSU02500" +"BSU02970" +"BSU03080" +"BSU03170" +"BSU03750" +"BSU03850" +"BSU03960" +"BSU03560" +"BSU03880" +"BSU05130" +"BSU05150" +"BSU05170" +"BSU05180" +"BSU05240" +"BSU05290" +"BSU05320" +"BSU05370" +"BSU05420" +"BSU05460" +"BSU05640" +"BSU05670" +"BSU05700" +"BSU05270" +"BSU06700" +"BSU07010" +"BSU07220" +"BSU06540" +"BSU06860" +"BSU08250" +"BSU08370" +"BSU08410" +"BSU07390" +"BSU08990" +"BSU09060" +"BSU09480" +"BSU10150" +"BSU10830" +"BSU10880" +"BSU13340" +"BSU13480" +"BSU13760" +"BSU13870" +"BSU16810" +"BSU18760" +"BSU18850" +"BSU19050" +"BSU19540" +"BSU18420" +"BSU21020" +"BSU18740" +"BSU21780" +"BSU21700" +"BSU26870" +"BSU26630" +"BSU26430" +"BSU28400" +"BSU29030" +"BSU30150" +"BSU29400" +"BSU30460" +"BSU30020" +"BSU31210" +"BSU32920" +"BSU33670" +"BSU33990" +"BSU34750" +"BSU34630" +"BSU34480" +"BSU35200" +"BSU33221" +"BSU38450" +"BSU38310" +"BSU38220" +"BSU37550" +"BSU36440" +"BSU36160" +"BSU36110" +"BSU40010" +"BSU39660" +"BSU38880" +"BSU40800" +"BSU40710" +"BSU40670" +"BSU40130" +"BSU25100" +"BSU25200" diff --git a/Inferelator/input/dream4/expression.tsv b/Inferelator/input/dream4/expression.tsv new file mode 100644 index 0000000..4727eff --- /dev/null +++ b/Inferelator/input/dream4/expression.tsv @@ -0,0 +1,101 @@ +"wt" "G1(-/-)" "G2(-/-)" "G3(-/-)" "G4(-/-)" "G5(-/-)" "G6(-/-)" "G7(-/-)" "G8(-/-)" "G9(-/-)" "G10(-/-)" "G11(-/-)" "G12(-/-)" "G13(-/-)" "G14(-/-)" "G15(-/-)" "G16(-/-)" "G17(-/-)" "G18(-/-)" "G19(-/-)" "G20(-/-)" "G21(-/-)" "G22(-/-)" "G23(-/-)" "G24(-/-)" "G25(-/-)" "G26(-/-)" "G27(-/-)" "G28(-/-)" "G29(-/-)" "G30(-/-)" "G31(-/-)" "G32(-/-)" "G33(-/-)" "G34(-/-)" "G35(-/-)" "G36(-/-)" "G37(-/-)" "G38(-/-)" "G39(-/-)" "G40(-/-)" "G41(-/-)" "G42(-/-)" "G43(-/-)" "G44(-/-)" "G45(-/-)" "G46(-/-)" "G47(-/-)" "G48(-/-)" "G49(-/-)" "G50(-/-)" "G51(-/-)" "G52(-/-)" "G53(-/-)" "G54(-/-)" "G55(-/-)" "G56(-/-)" "G57(-/-)" "G58(-/-)" "G59(-/-)" "G60(-/-)" "G61(-/-)" "G62(-/-)" "G63(-/-)" "G64(-/-)" "G65(-/-)" "G66(-/-)" "G67(-/-)" "G68(-/-)" "G69(-/-)" "G70(-/-)" "G71(-/-)" "G72(-/-)" "G73(-/-)" "G74(-/-)" "G75(-/-)" "G76(-/-)" "G77(-/-)" "G78(-/-)" "G79(-/-)" "G80(-/-)" "G81(-/-)" "G82(-/-)" "G83(-/-)" "G84(-/-)" "G85(-/-)" "G86(-/-)" "G87(-/-)" "G88(-/-)" "G89(-/-)" "G90(-/-)" "G91(-/-)" "G92(-/-)" "G93(-/-)" "G94(-/-)" "G95(-/-)" "G96(-/-)" "G97(-/-)" "G98(-/-)" "G99(-/-)" "G100(-/-)" "G1(+/-)" "G2(+/-)" "G3(+/-)" "G4(+/-)" "G5(+/-)" "G6(+/-)" "G7(+/-)" "G8(+/-)" "G9(+/-)" "G10(+/-)" "G11(+/-)" "G12(+/-)" "G13(+/-)" "G14(+/-)" "G15(+/-)" "G16(+/-)" "G17(+/-)" "G18(+/-)" "G19(+/-)" "G20(+/-)" "G21(+/-)" "G22(+/-)" "G23(+/-)" "G24(+/-)" "G25(+/-)" "G26(+/-)" "G27(+/-)" "G28(+/-)" "G29(+/-)" "G30(+/-)" "G31(+/-)" "G32(+/-)" "G33(+/-)" "G34(+/-)" "G35(+/-)" "G36(+/-)" "G37(+/-)" "G38(+/-)" "G39(+/-)" "G40(+/-)" "G41(+/-)" "G42(+/-)" "G43(+/-)" "G44(+/-)" "G45(+/-)" "G46(+/-)" "G47(+/-)" "G48(+/-)" "G49(+/-)" "G50(+/-)" "G51(+/-)" "G52(+/-)" "G53(+/-)" "G54(+/-)" "G55(+/-)" "G56(+/-)" "G57(+/-)" "G58(+/-)" "G59(+/-)" "G60(+/-)" "G61(+/-)" "G62(+/-)" "G63(+/-)" "G64(+/-)" "G65(+/-)" "G66(+/-)" "G67(+/-)" "G68(+/-)" "G69(+/-)" "G70(+/-)" "G71(+/-)" "G72(+/-)" "G73(+/-)" "G74(+/-)" "G75(+/-)" "G76(+/-)" "G77(+/-)" "G78(+/-)" "G79(+/-)" "G80(+/-)" "G81(+/-)" "G82(+/-)" "G83(+/-)" "G84(+/-)" "G85(+/-)" "G86(+/-)" "G87(+/-)" "G88(+/-)" "G89(+/-)" "G90(+/-)" "G91(+/-)" "G92(+/-)" "G93(+/-)" "G94(+/-)" "G95(+/-)" "G96(+/-)" "G97(+/-)" "G98(+/-)" "G99(+/-)" "G100(+/-)" "TS_1delt_0" "TS_1delt_50" "TS_1delt_100" "TS_1delt_150" "TS_1delt_200" "TS_1delt_250" "TS_1delt_300" "TS_1delt_350" "TS_1delt_400" "TS_1delt_450" "TS_1delt_500" "TS_2delt_500" "TS_2delt_550" "TS_2delt_600" "TS_2delt_650" "TS_2delt_700" "TS_2delt_750" "TS_2delt_800" "TS_2delt_850" "TS_2delt_900" "TS_2delt_950" "TS_2delt_1000" "TS_3delt_0" "TS_3delt_50" "TS_3delt_100" "TS_3delt_150" "TS_3delt_200" "TS_3delt_250" "TS_3delt_300" "TS_3delt_350" "TS_3delt_400" "TS_3delt_450" "TS_3delt_500" "TS_4delt_500" "TS_4delt_550" "TS_4delt_600" "TS_4delt_650" "TS_4delt_700" "TS_4delt_750" "TS_4delt_800" "TS_4delt_850" "TS_4delt_900" "TS_4delt_950" "TS_4delt_1000" "TS_5delt_0" "TS_5delt_50" "TS_5delt_100" "TS_5delt_150" "TS_5delt_200" "TS_5delt_250" "TS_5delt_300" "TS_5delt_350" "TS_5delt_400" "TS_5delt_450" "TS_5delt_500" "TS_6delt_500" "TS_6delt_550" "TS_6delt_600" "TS_6delt_650" "TS_6delt_700" "TS_6delt_750" "TS_6delt_800" "TS_6delt_850" "TS_6delt_900" "TS_6delt_950" "TS_6delt_1000" "TS_7delt_0" "TS_7delt_50" "TS_7delt_100" "TS_7delt_150" "TS_7delt_200" "TS_7delt_250" "TS_7delt_300" "TS_7delt_350" "TS_7delt_400" "TS_7delt_450" "TS_7delt_500" "TS_8delt_500" "TS_8delt_550" "TS_8delt_600" "TS_8delt_650" "TS_8delt_700" "TS_8delt_750" "TS_8delt_800" "TS_8delt_850" "TS_8delt_900" "TS_8delt_950" "TS_8delt_1000" "TS_9delt_0" "TS_9delt_50" "TS_9delt_100" "TS_9delt_150" "TS_9delt_200" "TS_9delt_250" "TS_9delt_300" "TS_9delt_350" "TS_9delt_400" "TS_9delt_450" "TS_9delt_500" "TS_10delt_500" "TS_10delt_550" "TS_10delt_600" "TS_10delt_650" "TS_10delt_700" "TS_10delt_750" "TS_10delt_800" "TS_10delt_850" "TS_10delt_900" "TS_10delt_950" "TS_10delt_1000" "TS_11delt_0" "TS_11delt_50" "TS_11delt_100" "TS_11delt_150" "TS_11delt_200" "TS_11delt_250" "TS_11delt_300" "TS_11delt_350" "TS_11delt_400" "TS_11delt_450" "TS_11delt_500" "TS_12delt_500" "TS_12delt_550" "TS_12delt_600" "TS_12delt_650" "TS_12delt_700" "TS_12delt_750" "TS_12delt_800" "TS_12delt_850" "TS_12delt_900" "TS_12delt_950" "TS_12delt_1000" "TS_13delt_0" "TS_13delt_50" "TS_13delt_100" "TS_13delt_150" "TS_13delt_200" "TS_13delt_250" "TS_13delt_300" "TS_13delt_350" "TS_13delt_400" "TS_13delt_450" "TS_13delt_500" "TS_14delt_500" "TS_14delt_550" "TS_14delt_600" "TS_14delt_650" "TS_14delt_700" "TS_14delt_750" "TS_14delt_800" "TS_14delt_850" "TS_14delt_900" "TS_14delt_950" "TS_14delt_1000" "TS_15delt_0" "TS_15delt_50" "TS_15delt_100" "TS_15delt_150" "TS_15delt_200" "TS_15delt_250" "TS_15delt_300" "TS_15delt_350" "TS_15delt_400" "TS_15delt_450" "TS_15delt_500" "TS_16delt_500" "TS_16delt_550" "TS_16delt_600" "TS_16delt_650" "TS_16delt_700" "TS_16delt_750" "TS_16delt_800" "TS_16delt_850" "TS_16delt_900" "TS_16delt_950" "TS_16delt_1000" "TS_17delt_0" "TS_17delt_50" "TS_17delt_100" "TS_17delt_150" "TS_17delt_200" "TS_17delt_250" "TS_17delt_300" "TS_17delt_350" "TS_17delt_400" "TS_17delt_450" "TS_17delt_500" "TS_18delt_500" "TS_18delt_550" "TS_18delt_600" "TS_18delt_650" "TS_18delt_700" "TS_18delt_750" "TS_18delt_800" "TS_18delt_850" "TS_18delt_900" "TS_18delt_950" "TS_18delt_1000" "TS_19delt_0" "TS_19delt_50" "TS_19delt_100" "TS_19delt_150" "TS_19delt_200" "TS_19delt_250" "TS_19delt_300" "TS_19delt_350" "TS_19delt_400" "TS_19delt_450" "TS_19delt_500" "TS_20delt_500" "TS_20delt_550" "TS_20delt_600" "TS_20delt_650" "TS_20delt_700" "TS_20delt_750" "TS_20delt_800" "TS_20delt_850" "TS_20delt_900" "TS_20delt_950" "TS_20delt_1000" +"G1" 0.1399892 0 0.1255953 0.1360556 0.1630365 0.1584708 0.141965 0.1019558 0.1815699 0.120485 0.3048511 0.1097646 0.1488869 0.1787071 0.1359591 0.1469732 0.1326039 0.1342363 0.1002457 0.1737404 0.1758758 0.1632499 0.0765289 0.1177987 0.1438789 0.1406035 0.1102006 0.1709398 0.0908405 0.1264221 0.1356573 0.1774658 0.1185442 0.2138496 0.1958515 0.1825262 0.3128081 0.3155642 0.1369814 0.1089901 0.1456417 0.1443003 0.2536811 0.1749183 0.1385526 0.1277068 0.3609378 0.1751445 0.0817863 0.1475597 0.1175013 0.1350555 0.1026374 0.1824513 0.1394689 0.1097338 0.0784205 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"TS_13delt_50" +TRUE "m" "TS_13delt_50" 50 "TS_13delt_100" +TRUE "m" "TS_13delt_100" 50 "TS_13delt_150" +TRUE "m" "TS_13delt_150" 50 "TS_13delt_200" +TRUE "m" "TS_13delt_200" 50 "TS_13delt_250" +TRUE "m" "TS_13delt_250" 50 "TS_13delt_300" +TRUE "m" "TS_13delt_300" 50 "TS_13delt_350" +TRUE "m" "TS_13delt_350" 50 "TS_13delt_400" +TRUE "m" "TS_13delt_400" 50 "TS_13delt_450" +TRUE "l" "TS_13delt_450" 50 "TS_13delt_500" +TRUE "f" NA NA "TS_14delt_500" +TRUE "m" "TS_14delt_500" 50 "TS_14delt_550" +TRUE "m" "TS_14delt_550" 50 "TS_14delt_600" +TRUE "m" "TS_14delt_600" 50 "TS_14delt_650" +TRUE "m" "TS_14delt_650" 50 "TS_14delt_700" +TRUE "m" "TS_14delt_700" 50 "TS_14delt_750" +TRUE "m" "TS_14delt_750" 50 "TS_14delt_800" +TRUE "m" "TS_14delt_800" 50 "TS_14delt_850" +TRUE "m" "TS_14delt_850" 50 "TS_14delt_900" +TRUE "m" "TS_14delt_900" 50 "TS_14delt_950" +TRUE "l" "TS_14delt_950" 50 "TS_14delt_1000" +TRUE "f" NA NA "TS_15delt_0" +TRUE "m" "TS_15delt_0" 50 "TS_15delt_50" +TRUE "m" "TS_15delt_50" 50 "TS_15delt_100" +TRUE "m" "TS_15delt_100" 50 "TS_15delt_150" +TRUE "m" "TS_15delt_150" 50 "TS_15delt_200" +TRUE "m" "TS_15delt_200" 50 "TS_15delt_250" +TRUE "m" "TS_15delt_250" 50 "TS_15delt_300" +TRUE "m" "TS_15delt_300" 50 "TS_15delt_350" +TRUE "m" "TS_15delt_350" 50 "TS_15delt_400" +TRUE "m" "TS_15delt_400" 50 "TS_15delt_450" +TRUE "l" "TS_15delt_450" 50 "TS_15delt_500" +TRUE "f" NA NA "TS_16delt_500" +TRUE "m" "TS_16delt_500" 50 "TS_16delt_550" +TRUE "m" "TS_16delt_550" 50 "TS_16delt_600" +TRUE "m" "TS_16delt_600" 50 "TS_16delt_650" +TRUE "m" "TS_16delt_650" 50 "TS_16delt_700" +TRUE "m" "TS_16delt_700" 50 "TS_16delt_750" +TRUE "m" "TS_16delt_750" 50 "TS_16delt_800" +TRUE "m" "TS_16delt_800" 50 "TS_16delt_850" +TRUE "m" "TS_16delt_850" 50 "TS_16delt_900" +TRUE "m" "TS_16delt_900" 50 "TS_16delt_950" +TRUE "l" "TS_16delt_950" 50 "TS_16delt_1000" +TRUE "f" NA NA "TS_17delt_0" +TRUE "m" "TS_17delt_0" 50 "TS_17delt_50" +TRUE "m" "TS_17delt_50" 50 "TS_17delt_100" +TRUE "m" "TS_17delt_100" 50 "TS_17delt_150" +TRUE "m" "TS_17delt_150" 50 "TS_17delt_200" +TRUE "m" "TS_17delt_200" 50 "TS_17delt_250" +TRUE "m" "TS_17delt_250" 50 "TS_17delt_300" +TRUE "m" "TS_17delt_300" 50 "TS_17delt_350" +TRUE "m" "TS_17delt_350" 50 "TS_17delt_400" +TRUE "m" "TS_17delt_400" 50 "TS_17delt_450" +TRUE "l" "TS_17delt_450" 50 "TS_17delt_500" +TRUE "f" NA NA "TS_18delt_500" +TRUE "m" "TS_18delt_500" 50 "TS_18delt_550" +TRUE "m" "TS_18delt_550" 50 "TS_18delt_600" +TRUE "m" "TS_18delt_600" 50 "TS_18delt_650" +TRUE "m" "TS_18delt_650" 50 "TS_18delt_700" +TRUE "m" "TS_18delt_700" 50 "TS_18delt_750" +TRUE "m" "TS_18delt_750" 50 "TS_18delt_800" +TRUE "m" "TS_18delt_800" 50 "TS_18delt_850" +TRUE "m" "TS_18delt_850" 50 "TS_18delt_900" +TRUE "m" "TS_18delt_900" 50 "TS_18delt_950" +TRUE "l" "TS_18delt_950" 50 "TS_18delt_1000" +TRUE "f" NA NA "TS_19delt_0" +TRUE "m" "TS_19delt_0" 50 "TS_19delt_50" +TRUE "m" "TS_19delt_50" 50 "TS_19delt_100" +TRUE "m" "TS_19delt_100" 50 "TS_19delt_150" +TRUE "m" "TS_19delt_150" 50 "TS_19delt_200" +TRUE "m" "TS_19delt_200" 50 "TS_19delt_250" +TRUE "m" "TS_19delt_250" 50 "TS_19delt_300" +TRUE "m" "TS_19delt_300" 50 "TS_19delt_350" +TRUE "m" "TS_19delt_350" 50 "TS_19delt_400" +TRUE "m" "TS_19delt_400" 50 "TS_19delt_450" +TRUE "l" "TS_19delt_450" 50 "TS_19delt_500" +TRUE "f" NA NA "TS_20delt_500" +TRUE "m" "TS_20delt_500" 50 "TS_20delt_550" +TRUE "m" "TS_20delt_550" 50 "TS_20delt_600" +TRUE "m" "TS_20delt_600" 50 "TS_20delt_650" +TRUE "m" "TS_20delt_650" 50 "TS_20delt_700" +TRUE "m" "TS_20delt_700" 50 "TS_20delt_750" +TRUE "m" "TS_20delt_750" 50 "TS_20delt_800" +TRUE "m" "TS_20delt_800" 50 "TS_20delt_850" +TRUE "m" "TS_20delt_850" 50 "TS_20delt_900" +TRUE "m" "TS_20delt_900" 50 "TS_20delt_950" +TRUE "l" "TS_20delt_950" 50 "TS_20delt_1000" diff --git a/Inferelator/input/dream4/tf_names.tsv b/Inferelator/input/dream4/tf_names.tsv new file mode 100644 index 0000000..df198ea --- /dev/null +++ b/Inferelator/input/dream4/tf_names.tsv @@ -0,0 +1,100 @@ +"G1" +"G2" +"G3" +"G4" +"G5" +"G6" +"G7" +"G8" +"G9" +"G10" +"G11" +"G12" +"G13" +"G14" +"G15" +"G16" +"G17" +"G18" +"G19" +"G20" +"G21" +"G22" +"G23" +"G24" +"G25" +"G26" +"G27" +"G28" +"G29" +"G30" +"G31" +"G32" +"G33" +"G34" +"G35" +"G36" +"G37" +"G38" +"G39" +"G40" +"G41" +"G42" +"G43" +"G44" +"G45" +"G46" +"G47" +"G48" +"G49" +"G50" +"G51" +"G52" +"G53" +"G54" +"G55" +"G56" +"G57" +"G58" +"G59" +"G60" +"G61" +"G62" +"G63" +"G64" +"G65" +"G66" +"G67" +"G68" +"G69" +"G70" +"G71" +"G72" +"G73" +"G74" +"G75" +"G76" +"G77" +"G78" +"G79" +"G80" +"G81" +"G82" +"G83" +"G84" +"G85" +"G86" +"G87" +"G88" +"G89" +"G90" +"G91" +"G92" +"G93" +"G94" +"G95" +"G96" +"G97" +"G98" +"G99" +"G100" diff --git a/Inferelator/input/ecoli/expression.tsv b/Inferelator/input/ecoli/expression.tsv new file mode 100644 index 0000000..abb8798 --- /dev/null +++ b/Inferelator/input/ecoli/expression.tsv @@ -0,0 +1,4512 @@ +"WT_1_1" "WT_2_2" "WT_3_3" "WT_4_4" "WT_5_5" "WT_6_6" "WT_7_7" "WT_8_8" "WT_9_9" "WT_10_10" "WT_11_11" "WT_12_12" "WT_13_13" "WT_14_14" "WT_15_15" "WT_16_16" "WT_17_17" "WT_18_18" "WT_19_19" "WT_20_20" "WT_21_21" "WT_22_22" "WT_23_23" "WT_24_49" "WT_25_51" "WT_26_55" "WT_27_58" "WT_28_60" "WT_29_63" "WT_30_65" "WT_31_67" "WT_32_69" "WT_33_71" "WT_34_288" "WT_35_295" "WT_36_297" "WT_37_299" "WT_38_302" "WT_39_304" "WT_40_309" "WT_41_311" "WT_42_316" "WT_43_320" "WT_44_322" "WT_45_340" "WT_46_342" "WT_47_344" "WT_48_346" "WT_49_348" "WT_50_350" "WT_51_355" "WT_52_357" "WT_53_366" "WT_54_367" "WT_55_369" "WT_56_371" "WT_57_373" "WT_58_378" "WT_59_380" "WT_60_385" "WT_61_389" "WT_62_391" "WT_63_393" "WT_64_417" "WT_65_425" "WT_66_429" "WT_67_433" "WT_68_437" "SS_1_50" "SS_2_52" "SS_3_53" "SS_4_54" "SS_5_56" "SS_6_57" "SS_7_59" "SS_8_61" "SS_9_62" "SS_10_64" "SS_11_66" "SS_12_68" "SS_13_70" "SS_14_72" "SS_15_289" "SS_16_290" "SS_17_291" "SS_18_292" "SS_19_293" "SS_20_294" "SS_21_296" "SS_22_298" "SS_23_300" "SS_24_301" "SS_25_303" "SS_26_305" "SS_27_306" "SS_28_307" "SS_29_308" "SS_30_310" "SS_31_312" "SS_32_313" "SS_33_314" "SS_34_315" "SS_35_317" "SS_36_318" "SS_37_319" "SS_38_321" "SS_39_323" "SS_40_324" "SS_41_325" "SS_42_326" "SS_43_327" "SS_44_328" "SS_45_329" "SS_46_330" "SS_47_331" "SS_48_332" "SS_49_333" "SS_50_334" "SS_51_335" "SS_52_336" "SS_53_337" "SS_54_338" "SS_55_339" "SS_56_341" "SS_57_343" "SS_58_345" "SS_59_347" "SS_60_349" "SS_61_351" "SS_62_352" "SS_63_353" "SS_64_354" "SS_65_356" "SS_66_358" "SS_67_359" "SS_68_360" "SS_69_361" "SS_70_362" "SS_71_363" "SS_72_364" "SS_73_365" "SS_74_368" "SS_75_370" "SS_76_372" "SS_77_374" "SS_78_375" "SS_79_376" "SS_80_377" "SS_81_379" "SS_82_381" "SS_83_382" "SS_84_383" "SS_85_384" "SS_86_386" "SS_87_387" "SS_88_388" "SS_89_390" "SS_90_392" "SS_91_394" "SS_92_418" "SS_93_419" "SS_94_420" "SS_95_421" "SS_96_422" "SS_97_423" "SS_98_424" "SS_99_426" "SS_100_427" "SS_101_428" "SS_102_430" "SS_103_431" "SS_104_432" "SS_105_434" "SS_106_435" "SS_107_436" "SS_108_438" "SS_109_439" "TS_1_24_t0" "TS_2_25_t80" "TS_3_26_t300" "TS_4_27_t600" "TS_5_28_t960" "TS_6_29_t1440" "TS_7_30_t2160" "TS_8_31_t3120" "TS_9_32_t300" "TS_10_33_t360" "TS_11_34_t450" "TS_12_35_t740" "TS_13_36_t810" "TS_14_37_t480" "TS_15_38_t840" "TS_16_39_t1800" "TS_17_40_t2880" "TS_18_41_t0" "TS_19_42_t80" "TS_20_43_t0" "TS_21_44_t80" "TS_22_45_t480" "TS_23_46_t840" "TS_24_47_t1800" "TS_25_48_t2880" "TS_26_73_t0" "TS_27_74_t60" "TS_28_75_t120" "TS_29_76_t240" "TS_30_77_t0" "TS_31_78_t60" "TS_32_79_t120" "TS_33_80_t240" "TS_34_81_t0" "TS_35_82_t60" "TS_36_83_t120" "TS_37_84_t240" "TS_38_85_t0" "TS_39_86_t60" "TS_40_87_t120" "TS_41_88_t240" "TS_42_89_t0" "TS_43_90_t60" "TS_44_91_t120" "TS_45_92_t240" "TS_46_93_t60" "TS_47_94_t120" "TS_48_95_t240" "TS_49_96_t60" "TS_50_97_t120" "TS_51_98_t240" "TS_52_99_t0" "TS_53_100_t60" "TS_54_101_t120" "TS_55_102_t240" "TS_56_103_t60" "TS_57_104_t120" "TS_58_105_t240" "TS_59_106_t0" "TS_60_107_t2" "TS_61_108_t10" "TS_62_109_t20" "TS_63_110_t300" "TS_64_111_t360" "TS_65_112_t420" "TS_66_113_t480" "TS_67_114_t540" "TS_68_115_t600" "TS_69_116_t660" "TS_70_117_t720" "TS_71_118_t960" "TS_72_119_t300" "TS_73_120_t360" "TS_74_121_t420" "TS_75_122_t480" "TS_76_123_t540" "TS_77_124_t600" "TS_78_125_t660" "TS_79_126_t720" "TS_80_127_t960" "TS_81_128_t60" "TS_82_129_t120" "TS_83_130_t60" "TS_84_131_t120" "TS_85_132_t60" "TS_86_133_t120" "TS_87_134_t60" "TS_88_135_t120" "TS_89_136_t60" "TS_90_137_t120" "TS_91_138_t0" "TS_92_139_t60" "TS_93_140_t120" "TS_94_141_t0" "TS_95_142_t60" "TS_96_143_t120" "TS_97_144_t180" "TS_98_145_t240" "TS_99_146_t60" "TS_100_147_t120" "TS_101_148_t180" "TS_102_149_t240" "TS_103_150_t60" "TS_104_151_t120" "TS_105_152_t180" "TS_106_153_t240" "TS_107_154_t48" "TS_108_155_t120" "TS_109_156_t0" "TS_110_157_t24" "TS_111_158_t48" "TS_112_159_t72" "TS_113_160_t96" "TS_114_161_t120" "TS_115_162_t0" "TS_116_163_t60" "TS_117_164_t120" "TS_118_165_t240" "TS_119_166_t60" "TS_120_167_t120" "TS_121_168_t240" "TS_122_169_t840" "TS_123_170_t1800" "TS_124_171_t2880" "TS_125_172_t840" "TS_126_173_t1800" "TS_127_174_t2880" "TS_128_175_t0" "TS_129_176_t120" "TS_130_177_t480" "TS_131_178_t0" "TS_132_179_t120" "TS_133_180_t480" "TS_134_181_t120" "TS_135_182_t480" "TS_136_183_t120" "TS_137_184_t480" "TS_138_185_t0" "TS_139_186_t60" "TS_140_187_t120" "TS_141_188_t60" "TS_142_189_t120" "TS_143_190_t60" "TS_144_191_t120" "TS_145_192_t60" "TS_146_193_t120" "TS_147_194_t60" "TS_148_195_t120" "TS_149_196_t0" "TS_150_197_t720" "TS_151_198_t840" "TS_152_199_t960" "TS_153_200_t0" "TS_154_201_t120" "TS_155_202_t240" "TS_156_203_t360" "TS_157_204_t720" "TS_158_205_t840" "TS_159_206_t960" "TS_160_207_t0" "TS_161_208_t60" "TS_162_209_t120" "TS_163_210_t60" "TS_164_211_t120" "TS_165_212_t60" "TS_166_213_t120" "TS_167_214_t0" "TS_168_215_t10" "TS_169_216_t40" "TS_170_217_t80" "TS_171_218_t0" "TS_172_219_t10" "TS_173_220_t40" "TS_174_221_t80" "TS_175_222_t0" "TS_176_223_t10" "TS_177_224_t40" "TS_178_225_t80" "TS_179_226_t0" "TS_180_227_t10" "TS_181_228_t40" "TS_182_229_t80" "TS_183_230_t0" "TS_184_231_t120" "TS_185_232_t480" "TS_186_233_t120" "TS_187_234_t480" "TS_188_235_t0" "TS_189_236_t120" "TS_190_237_t480" "TS_191_238_t120" "TS_192_239_t480" "TS_193_240_t120" "TS_194_241_t360" "TS_195_242_t0" "TS_196_243_t120" "TS_197_244_t360" "TS_198_245_t0" "TS_199_246_t120" "TS_200_247_t360" "TS_201_248_t0" "TS_202_249_t60" "TS_203_250_t120" "TS_204_251_t240" "TS_205_252_t0" "TS_206_253_t60" "TS_207_254_t120" "TS_208_255_t240" "TS_209_256_t60" "TS_210_257_t120" "TS_211_258_t240" "TS_212_259_t60" "TS_213_260_t120" "TS_214_261_t240" "TS_215_262_t60" "TS_216_263_t120" "TS_217_264_t240" "TS_218_265_t60" "TS_219_266_t120" "TS_220_267_t240" "TS_221_268_t0" "TS_222_269_t20" "TS_223_270_t20" "TS_224_271_t20" "TS_225_272_t20" "TS_226_273_t20" "TS_227_274_t20" "TS_228_275_t20" "TS_229_276_t0" "TS_230_277_t120" "TS_231_278_t220" "TS_232_279_t360" "TS_233_280_t0" "TS_234_281_t120" "TS_235_282_t220" "TS_236_283_t360" "TS_237_284_t0" "TS_238_285_t120" "TS_239_286_t220" "TS_240_287_t360" "TS_241_395_t480" "TS_242_396_t1800" "TS_243_397_t2880" "TS_244_398_t480" "TS_245_399_t1800" "TS_246_400_t2880" "TS_247_401_t180" "TS_248_402_t270" "TS_249_403_t660" "TS_250_404_t960" "TS_251_405_t1440" "TS_252_406_t0" "TS_253_407_t480" "TS_254_408_t960" "TS_255_409_t180" "TS_256_410_t300" "TS_257_411_t480" "TS_258_412_t720" "TS_259_413_t180" "TS_260_414_t300" "TS_261_415_t480" "TS_262_416_t720" "TS_263_440_t0" "TS_264_441_t60" "TS_265_442_t120" "TS_266_443_t240" "TS_267_444_t360" "TS_268_445_t0" "TS_269_446_t60" "TS_270_447_t120" "TS_271_448_t240" "TS_272_449_t360" "TS_273_450_t0" "TS_274_451_t60" "TS_275_452_t120" "TS_276_453_t240" "TS_277_454_t360" "TS_278_455_t0" "TS_279_456_t60" "TS_280_457_t120" "TS_281_458_t240" "TS_282_459_t360" "TS_283_460_t0" "TS_284_461_t60" "TS_285_462_t120" "TS_286_463_t240" "TS_287_464_t360" "TS_288_465_t0" "TS_289_466_t60" "TS_290_467_t120" "TS_291_468_t240" "TS_292_469_t360" 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9.5903 8.9917 8.4709 +"G7" 9.39995 9.3896 8.87085 9.1011 9.23505 11.04 12.4883333 13.1525 12.5663333 12.9925 8.7438 7.1703 8.4169 13.144 11.259 10.525 7.2814333 9.21505 8.6565 12.544 8.723 12.1293333 8.7031 9.5011 9.8711 9.7593333 12.17 10.626 9.73236 13.2855 10.019 9.5247 11.626 10.136 13.447 11.703 8.6211 10.023 9.44438 11.306 9.15215 11.802 9.5084 13.22 8.5209 7.6603 10.213 11.622 12.644 10.2577 11.04 10.443 9.6001 11.5073333 12.521 9.3896 12.2865 11.975 10.3756667 10.618 9.1067667 7.6603 13.275 12.9555 10.8393333 13.466 11.04 11.538 7.9232 10.178 9.06785 9.5116 9.0060333 12.8806 12.754 10.447 8.2515 8.36758 12.6895 9.7058 12.122 12.164 9.4607667 10.0437333 9.6142667 10.2965667 9.4671667 9.7156333 12.477 11.4163333 8.4436 9.5901 9.6493 9.3777667 10.034 9.2267333 9.9823 10.687 8.4063 8.0874 8.1672 9.6634 11.958 12.021 12.13 8.8487 9.6678 10.98 10.676 11.082 9.6984 10.084 10.941 11.241 11.222 11.282 8.0568 10.107 11.35 8.5509 11.3296667 12.4006667 9.8156 8.1383333 8.1111 9.188525 12.278 12.453 12.066 10.053 9.7244 10.147 9.9149 10.459 8.9004 9.8348667 9.0588 9.579 8.9154 9.2806 9.9084333 12.643 10.739 9.4243667 10.871 12.583 12.182 12.028 12.607 12.836 8.4488 8.3051 9.0964 10.583 10.544 10.184 9.9099667 8.0043 12.108 12.9585 13.0415 12.9815 8.66175 10.2905 10.7555 10.516 12.5733333 11.7786667 12.105 13.5016667 13.6116667 13.4103333 8.3211 11.157 11.7955 12.064 12.03 9.81 9.6469 9.1391 7.66485 8.42755 8.43285 8.87405 8.0974 9.2096 7.6639 7.9949 7.77935 8.48055 12.831 12.717 8.8815 7.6524 10.186 10.399 10.34 9.7576 11.661 12.371 9.9451 10.513 12.367 12.56 12.661 11.758 12.226 12.429 12.517 12.344 12.036 12.154 11.89 11.897 12.207 12.291 12.004 11.942 13.103 12.582 10.881 8.5905 12.938 12.263 11.748 12.917 12.615 12.271 12.395 11.629 9.193 8.3049 11.548 8.2843 9.0736 11.2308 11.002 10.2131 10.272 12.662 12.825 12.887 12.576 11.823 10.128 10.903 9.8817 9.5085 12.516 12.651 12.347 11.07 11.103 10.84 10.384 9.9219 11.81 13.2 12.178 12.745 10.292 13.132 11.906 12.886 11.922 12.779 12.066 13.156 13.028 11.984 12.155 10.292 10.22 8.2895 8.6568 10.161 10.148 8.6204 8.6563 11.082 10.543 10.785 11.043 9.6554 9.208 10.5596667 8.4348 9.0458 9.8811333 10.2532 10.5503333 12.843 13.136 13.041 12.789 13.096 13.107 13.028 13.144 11.306 10.581 11.588 11.582 10.165 10.6623333 11.4153333 11.98 12.189 12.6346667 10.4496667 10.4956667 9.8799 8.7483667 9.5523 11.466 8.0966 7.9167 9.9727 9.6958 9.8602 9.1493 10.039 12.085 11.106 11.994 11.871 12.676 13.026 11.645 12.145 8.2577 8.0369 7.8904 8.3374 11.631 9.0844 12.959 9.8042 9.9682 10.193 10.723 10.291 11.079 11.3056667 11.4515 10.5498667 9.73365 11.265 11.7535 9.6249 9.8033 11.296 11.448 9.6352 9.5662667 11.6613333 11.816 10.381 9.9989 10.6623333 11.4153333 11.98 10.4956667 9.8799 12.2316 12.6346667 10.4496667 8.7483667 9.5523 10.998 12.759 13.321 11.563 13.274 13.389 10.86 12.811 12.872 12.093 11.941 11.948 12.658 11.595 10.974 10.26 11.813 12.165 11.955 11.208 11.809 11.991 10.078 8.9613 8.8743 12.807 12.752 12.926 12.873 12.594 12.496 12.804 12.546 13.164 12.999 12.744 11.244 11.2035 8.34955 10.3945 11.483 10.1235 8.4503 9.8554 10.961 10.572 8.44585 10.333 11.805 12.521 11.04 12.585 12.512 11.123 9.8933 8.11025 8.16605 8.08285 8.04585 10.1507333 8.3783 8.3753 13.3493333 12.5623333 12.2016667 11.3226667 13.1256667 12.4563333 10.9325 11.9653333 12.786 11.839 11.806 11.727 10.961 13.074 11.546 11.149 10.58 11.517 13.329 12.079 11.53 11.061 9.6963 12.888 12.114 11.841 12.194 11.694 12.419 11.966 11.429 11.652 11.947 13.062 12.749 11.646 11.887 12.129 12.865 12.027 11.71 12.28 12.875 12.1405 11.8575 12.471 10.03 9.4597 9.0256 9.9707 9.966 9.7552 12.756 12.916 12.821 12.284 +"G8" 8.2084 9.0054667 8.51015 8.73635 8.78815 8.3849 8.8959667 8.2654 8.7168 8.4925 8.55555 10.128 8.78035 7.7087 8.4182 8.6066 7.7226333 8.62355 8.2862 8.08025 8.6538 8.8217 8.6647 8.5371 7.8243 8.9936667 8.59866 8.3616 8.36968 8.1871 8.6366 8.0134 8.6101 8.6349667 8.5987 8.9535667 8.40325 8.3311 8.6245 7.3848 8.4063 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9.6941 10.15 8.4808 11.223 10.752 8.7968 10.543 8.7722 10.403 9.9266 11.459 8.7791 9.7569 8.1353 8.4451 11.094 10.055 8.5985 10.92 10.669 8.9013 9.5018667 8.9652 9.5860667 9.6644 10.9616667 11.1036667 9.7595 10.0364333 10.575 8.9538 11.111 8.6509 8.3519 6.6129 11.333 7.0656 10.062 8.1886 8.6855 10.592 10.217 9.2366333 10.2174667 10.8103333 9.1216667 9.2213667 10.596 9.2985667 8.996 9.8173333 9.5270333 8.6557 10.02 9.4754 10.002 11.009 8.3518 8.4686 10.028 11.135 10.855 11.228 9.1056 8.1886 11.508 10.82 10.125 10.363 10.699 8.5913 8.5277 11.469 8.5084 11.232 11.116 10.335 8.3647 11.845 11.726 10.661 11.4773333 11.3285 10.8766333 9.94045 10.238 10.1309 9.2328 11.1913333 10.51815 8.7828 10.7585 9.5347667 10.4326667 11.4396667 10.995 10.5561 9.8238 10.3786 9.3675333 9.9769333 8.4283 10.62744 11.1866667 10.7986667 10.2397333 8.7555333 8.521 8.195 10.393 10.143 10.119 10.195 10.833 9.6681 9.8976 10.248 8.4386 10.238 10.718 10.02 10.648 8.122 10.002 8.4728 9.6235 11.744 8.2884 11.475 8.1858 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7.5964 7.5620333 7.9699667 7.9665667 7.6225 7.6429 7.4652 7.6056 7.0462 7.4464 7.6101 7.2389 6.7289 6.8672 6.5506 9.4331 6.9931 8.556 9.9815 9.1105 8.2682 7.7882 8.0736 7.9165 7.462 7.6087 8.2597 8.6673 7.3434 7.2817 7.0199 7.1315 6.9482 6.2592667 6.5491 7.7053 8.2246 6.40625 6.209 7.9064 8.0757667 6.29815 6.6835 8.4757 7.8390667 6.1934 6.5704333 8.0057333 7.848375 7.6783667 7.5003 6.9321667 7.5964 7.5620333 6.56614 6.3065333 7.3883333 7.9699667 7.9665667 7.4568 7.6971 8.1564 8.1211 7.802 6.8688 7.4137 7.9922 6.7128 6.5599 6.2585 6.6922 8.1832 8.0654 8.003 7.6405 8.1682 8.2409 7.0885 8.7018 7.162 6.7956 7.3268 7.8137 7.7889 7.5484 7.505 7.4345 8.2 8.767 8.5434 7.8722 7.9216 6.7416 6.4951 7.0162 7.47955 6.24415 10.3855 6.0675 8.5415 6.0585 10.479 6.1884 7.85875 6.5469 10.342 6.1513 11.155 8.7963 7.9053 7.859 7.9285 7.3673 6.8663333 8.9324 7.67225 7.45835 7.23235 6.6449667 7.90665 6.9827 7.0159667 7.4521667 7.7893667 7.4736333 6.4438 8.044 7.6289 7.7365667 8.56 9.3413 10.738 8.1503 8.3715 7.2964 8.8766 10.838 9.9736 7.2777 7.1432 8.3565 9.2748 9.1027 7.1438 6.9925 8.1452 7.5048 9.0486 7.9838 7.1895 7.4193 8.0091 7.1729 6.5651 8.3225 6.8157 8.2094 7.7359 8.3194 6.5528 7.1751 6.7889 7.3076 6.84455 7.61365 6.53845 6.5432 6.7556 6.4886 6.0299 6.1107 6.0945 6.8311 6.0848 6.1423 6.4077 6.2609 +"G17" 10.7835 10.0192333 10.3945 10.858 10.726 9.496 9.7582667 11.0995 9.961 10.862 10.374 8.8396 10.7325 9.5952 9.9861 10.1835 8.3813 10.7245 10.702 10.55 11.08 9.6592667 10.8955 9.7738 11.187 10.0239333 10.4972 10.998 10.7696 10.5685 11.408 9.6516 10.706 11.229 11.516 10.1546667 10.666 11.3915 10.3534 9.0882 11.1 11.148 9.6841 10.896 10.5493333 10.742 10.7073333 11.862 10.988 11.187 10.319 11.3025 11.364 10.2326333 11.06 10.9486667 10.721 11.564 9.1604 10.801 9.5866 10.742 9.6619 10.771 11.742 11.226 10.319 11.054 8.9031 11.027 10.447 10.502 10.4763333 10.339 10.4104 10.888 11.1246 10.9994 9.61195 11.377 9.7377 10.828 11.31 11.0606667 11.1853333 11.2106667 11.4413333 11.0696667 9.0215 10.1436667 10.598 9.5473 11.4055 10.868 10.525 10.0334667 10.406 10.256 10.817 11.369 10.982 10.7365 10.935 11.16 11.042 9.116 11.428 11.47 11.024 11.498 10.976 11.201 11.343 11.227 11.396 10.48 11.343 10.695 10.954 10.795 11.0536667 10.4106667 10.6866667 10.7856667 10.067 10.91625 11.02 11.307 11.144 11.101 10.793 11.058 10.557 11.535 10.486 11.194 11.2626667 11.4116667 11.5616667 11.223 11.2326667 10.6316667 10.867 10.984 10.644 10.675 10.833 10.415 11.351 9.9737 11.823 12.099 11.957 10.999 11.505 10.331 10.6433333 10.076 9.6103 10.53 10.715 11.11 10.5275 10.9995 10.754 10.6145 11.729 11.3886667 11.2093333 10.8296667 10.8426667 10.6266667 9.7699 10.357 9.83285 11.114 11.288 8.9826 8.7194 10.8845 11.1825 11.2275 11.1885 11.094 10.434 11.125 11.2495 11.049 10.7215 10.4405 10.597 11.08 10.309 10.339 9.0035 8.6684 8.9757 9.0326 10.773 11.115 10.778 9.8021 10.718 10.534 10.784 10.713 10.758 10.573 10.441 10.422 10.853 11.011 11.134 11.223 10.792 10.934 11.036 11.17 10.603 10.576 10.477 11.255 10.461 9.0027 9.4568 9.6363 9.6731 9.8464 10.728 10.862 11.28 11.307 10.794 11.205 11.149 10.2458 10.5662 10.9192 10.4694 10.537 10.583 10.288 10.967 10.552 11.143 11.282 11.368 11.136 10.441 10.467 10.49 11.125 11.116 11.557 11.205 11.491 10.302 10.475 10.428 10.7 10.874 10.762 10.737 10.392 10.429 10.754 10.175 10.5 10.753 10.602 10.444 10.894 10.694 10.984 10.878 10.882 10.719 10.843 11.167 10.504 10.586 10.452 10.452 10.8026667 11.012 10.7696667 10.8563333 11.0633333 10.8236667 10.418 10.2673333 10.756 9.7226 9.8405 9.8555 9.815 9.7203 9.7203 9.5952 9.0882 9.7756 9.6612 9.9983 9.4627 10.6766667 11.013 10.9463333 10.9246667 10.926 9.4799333 10.4893333 10.584 9.9913667 9.5138667 9.7027 8.5832 8.2085 10.403 10.549 10.969 11.046 10.212 9.1127 9.3179 9.0279 10.893 10.491 10.71 10.887 10.617 11.058 11.073 11.158 10.591 10.802 10.811 10.779 10.757 10.28 10.204 9.468 10.133 9.0373 10.5656667 10.28175 10.6466667 10.53075 10.53 10.62 10.693 10.6216667 10.52 10.528 10.7825 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7.9581 7.45085 7.9391 7.79615 8.6173 7.7934667 7.3487 7.7213667 7.3943 7.97675 8.5726 8.00495 8.9852 8.8585 7.67025 7.9813667 8.12655 6.858 7.37795 7.9823 7.562 8.22585 7.6655 7.4252 7.8874667 7.91268 7.4441 7.74352 7.4582 7.7311 7.9339 7.6749 7.7522333 7.6496 7.5742 7.76455 7.474 7.68064 7.9664 7.7526 7.5523 7.647 7.1291 7.7227333 7.9245 7.5702 7.6339 7.5632 7.4788333 7.0937 7.3156 7.833425 7.7224667 8.0112 7.3684 7.7478 7.8452 7.6874667 7.9565 7.9891 7.9245 7.447 7.97435 7.5432667 7.1673 7.0937 7.7502 8.2697 7.6388 7.9718 7.6366 7.9441333 7.54386 7.6494 7.5596 7.6625 7.65824 8.43445 7.3752 7.5071 7.6903 7.7460333 7.7333667 7.5251667 7.6471333 7.7104667 7.7613333 7.5813 7.5008 7.6943 8.2375 7.79325 7.5839333 7.4124 7.8471667 7.9934 7.9023 7.9432 7.5455 7.8864 7.6871 7.5664 7.0108 7.7053 8.1272667 7.5001 7.8513 7.5632 7.5 7.1933 7.6274 7.3653 7.5509 7.2265 7.5062 7.5475 7.5621 7.4112 7.7354 7.2819667 7.4438667 7.7650333 7.5997667 7.5227 7.693925 7.2481 7.7467 7.2846 7.607 7.4704 7.3944 7.8787 7.6449 7.43005 7.8350333 7.6736333 7.7500667 7.7714333 7.6310667 7.6795 7.8301667 7.5739 7.7139 7.836 7.9008 7.2137 7.9092 7.5378 7.2352 7.2014 7.6408 7.8693 7.8556 7.9672 7.6884 7.4521333 7.6683 7.6497 7.46595 7.35065 7.5606 8.0649 7.5841 7.72275 7.5476 7.4196333 7.3751333 7.4577333 7.4987667 7.3594667 7.3129667 7.9495 7.84 7.2974 7.8087 7.4848 7.5515 7.4092 7.665 7.60145 7.6371 7.64215 7.5171 7.9388 7.78285 7.7922 7.85925 7.90505 7.6305 7.4746 7.0652 7.9134 8.0514 8.1355 7.5468 7.9206 7.8214 7.6445 7.7518 7.7835 8.3429 7.425 7.5518 7.5812 7.4012 7.647 7.4958 7.6009 7.6563 7.329 7.4317 7.7431 7.5186 7.4237 7.1322 7.5097 7.5142 7.1345 7.4196 7.3466 7.5624 7.2014 7.4604 7.6796 7.9854 8.3028 8.0707 7.5718 7.7851 7.6788 7.1872 7.4185 7.6377 7.8831 7.61648 7.75784 7.76396 7.67392 7.1855 7.4397 7.0042 7.5039 7.3257 7.1548 7.3877 7.6709 7.5119 7.3994 7.5341 7.2988 7.4301 7.2615 7.3786 7.5193 7.6469 7.3792 7.3807 7.4627 7.4541 7.4944 7.3572 7.5413 7.2609 7.4897 7.3235 7.5815 7.1201 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9.95375 9.938 10.11415 9.6629 10.4445 10.086 9.6017333 9.5520333 10.0204333 10.4083333 9.4262 9.891 10.6046667 10.5113333 9.4683 9.7046 9.3795 10.434 10.099 9.3514 9.4075 9.3437 9.6422 9.462 9.3996 9.4809 9.653 9.9977 10.2 9.3862 9.6371 9.4259 9.5034 9.6744 9.6103 9.6764 9.8479 9.749 9.9676 9.3483 9.3894 9.7316 9.6237 9.6483 9.3368 9.5062 9.5101 10.023 9.43005 9.21225 9.3777 9.5152 9.4148 9.8188 9.9288 10.77 10.435 9.4295 9.5946 9.5765 9.4571 9.6866 +"G25" 8.5983 8.2629667 8.44975 8.6049 8.60375 8.7191 8.1654 8.07785 7.9635333 8.24535 8.7482 8.4042 8.41035 9.3336 8.8585 8.71165 7.9457333 8.7231 7.8225 8.427 8.30055 7.5056667 8.51475 8.7662 8.4818 9.2290333 8.92744 9.1456 8.54868 8.4914 8.5483 9.6035 8.7408 8.1709333 8.4882 8.8662 8.4531 9.386 8.4401 9.0257 8.60595 9.086 8.9657333 8.369 8.2928 8.3472 8.3607333 8.3207 7.9281 8.4066 8.5986 8.5076 8.204575 9.0747333 8.5711 8.3289333 9.318 9.4314 7.9709667 9.6192 8.913 8.3472 8.8967 9.1654 9.3454667 8.6259333 8.5986 8.732 8.6118 8.6244 8.5307 8.39205 8.7580333 8.66422 8.84604 8.9671 8.47022 8.53928 9.41745 8.2867 8.8313 9.1348 8.1901667 8.2797333 8.1505333 8.2097667 8.0773333 8.3102 8.6882 8.6851333 8.638 8.473 9.4603 8.2653667 8.3816 8.4266667 8.3736 9.0528 8.6074 8.377 8.3856 8.75085 9.032 9.0019 8.7735 8.8948 8.4898 8.4977 9.2173 8.525 8.5787 9.3546 8.4986 8.6375 8.5577 8.7701 8.914 9.8935 8.6589 8.675 8.9208 8.9149667 8.5575333 8.5842333 8.9516 8.44605 8.876 8.8459 8.4221 8.329 8.0783 8.2303 8.5706 8.4527 8.44745 8.2764333 8.2854333 8.1260333 8.1152 8.2511333 8.2305 8.9141333 8.5964 8.5224333 9.4651 9.3815 9.6673 9.0417 9.014 7.8953 8.1349 8.1393 8.2574 9.3336 9.3449 9.3486 7.9700333 8.8407 8.9635 9.3261 9.15215 9.18295 8.9319 8.77685 9.4857 8.935 9.0091333 9.3507333 9.0464 9.5024 8.5017333 9.2462 8.7076 8.9124 9.21955 8.9512 8.4088 8.6502 8.6442 8.75955 8.3958 8.2988 8.4091 8.18055 8.4361 8.50095 8.44155 8.202 8.35775 8.14055 8.4554 8.3536 8.5748 8.7273 9.0478 8.8996 9.2787 8.4789 8.5981 8.5173 8.7577 8.6042 9.7039 9.3362 10.093 8.8158 9.9039 10.072 10.381 9.8447 9.7289 9.034 8.8684 9.0246 9.6232 8.9136 8.8182 8.8495 8.6078 8.7398 8.4944 8.7661 8.7373 8.4675 8.406 8.0413 7.7789 7.8615 8.823 9.3006 8.5629 8.5207 9.4673 8.3738 8.5985 9.02404 9.23708 9.04992 9.3737 8.3434 8.5672 8.5763 8.5757 8.6302 8.6677 8.2114 8.5511 9.1021 8.407 8.5958 8.716 8.2394 8.1665 8.2275 8.4832 8.1943 8.3216 8.616 8.6611 9.8322 8.8456 10.69 9.2625 9.9737 9.4023 10.689 9.2672 8.6247 10.164 8.6544 8.6696 8.8305 8.8713 9.2664 9.0759 8.9595 8.8585 9.4269 8.8976 9.2226 8.7874 8.7214 8.9897 8.5439667 8.3018333 8.5983667 8.4743667 8.489 8.4968 8.4229667 8.4025333 8.6253 7.7628 7.7982 7.8181 7.6652 8.1644 8.123 9.3336 9.0257 8.9325 8.8002 8.7046 8.8997 8.5776 8.6638333 8.832 8.2397 8.4329 9.276 8.7142333 8.6885 9.1750667 9.3503 8.8324 9.0189 8.8512 8.2044 8.6458 8.0254 8.5323 8.2851 8.0664 8.2434 8.3709 8.9203 9.894 8.727 8.9188 8.8105 8.6761 8.8761 8.659 9.0548 9.0176 8.9831 9.7188 8.7274 8.5495 8.5079 8.3525 8.3312 8.3201 8.6487667 8.88165 9.2061667 8.91975 8.76175 8.81515 9.19295 8.9018 8.59415 9.00065 9.2338 8.8561667 8.5697333 8.8223667 9.1642333 8.749275 8.5776 8.6638333 8.832 8.7142333 8.6885 8.2578 8.4329 9.276 9.1750667 9.3503 8.4696 8.7687 8.5575 8.4891 8.4868 8.475 8.3133 8.5406 9.4595 8.8735 8.6631 8.752 8.8341 8.6787 8.7895 8.5515 8.7567 9.3052 9.0295 8.6664 8.9171 7.6435 9.3731 9.4273 8.606 8.1995 8.1225 7.6071 8.8683 8.7997 8.8908 8.6926 8.8184 8.427 8.0321 8.1597 8.3247 8.42785 9.23265 8.2244 8.38785 8.4326 8.9392 8.80045 8.7182 8.47135 9.1438 8.54715 9.5274 8.5711 8.5986 8.6679 8.4704 8.6411 8.2101667 8.50105 8.338 8.73005 8.76305 8.4482 8.55545 8.3646 8.4824333 8.6379 8.8435333 8.8509333 8.3821667 8.5260333 8.8078667 8.7725667 9.6406 8.998 9.2989 8.5118 8.4741 10.112 9.3987 10.229 9.2774 9.1335 8.4959 8.9959 8.9418 8.8242 8.219 8.4928 8.8925 8.5941 9.1021 8.8458 8.5189 8.8445 8.8911 8.644 9.0785 9.862 8.4766 8.7314 8.5121 8.7005 8.8169 8.4586 8.2485 8.3432 8.32565 8.98135 8.55875 8.9953 8.5966 8.1832 8.1545 8.0782 7.7659 8.3627 8.2015 7.4301 7.9069 7.7741 +"G26" 9.15955 8.3546 9.3436 8.26485 9.93135 8.5432 8.2663667 9.1106 8.5172667 9.0032 9.3094 8.5177 9.47905 8.4899 8.8001 9.07405 8.5384 9.41615 9.6049 9.33595 9.2089 8.4612667 9.7879 8.6212 9.9781 9.6425 9.26024 10.35 10.1856 8.89375 10.556 9.1318 10.523 9.4076333 9.0844 8.1742667 9.3542 9.2642 8.94964 8.2615 9.4145 10.358 8.5547 9.7542 9.3462333 9.3807 9.1643667 8.8284 9.166 9.1588 9.2199 9.68215 9.4854 8.3523 10.532 9.2740667 10.213 9.2455 8.9733333 9.7439 8.4471333 9.3807 10.343 9.88785 8.7364333 9.9289 9.2199 9.256 8.6789 10.117 8.73445 8.539 9.8403 9.58214 9.44606 9.8225 9.29238 9.68404 8.44465 10.789 9.0547 10.671 9.1292667 9.1667667 9.1112667 9.1570667 9.0292667 9.0980333 9.6975 9.1834 9.024 8.7722 9.32455 8.7124667 9.1669 8.3838667 9.1628 9.1976 8.9249 9.1543 9.9204 9.34185 10.356 10.547 10.493 8.5605667 10.06 9.6549 10.807 9.9176 9.7723 10.708 9.9022 9.855 9.6339 10.739 9.5943 10.583 9.9962 9.7328 8.7003 8.8916667 9.2268333 10.3854333 9.0922 9.096225 8.9294 8.8035 9.195 9.0374 8.7968 8.9876 9.4714 9.6208 8.97175 9.6546 9.3434667 9.0622 9.3019 9.6179 9.5690333 8.7222667 9.0841 10.0170333 9.9748 10.493 10.453 9.1611 9.4913 8.7777 8.9676 8.6161 8.8107 9.6117 9.7187 9.6275 9.5870333 9.0663 9.7422 9.8841 9.79535 9.4015 9.4028 10.7085 9.683 9.2953 9.6088 9.7192 9.7673667 9.6235333 10.2486667 9.4598 9.1013 9.3618 9.3453 9.2736 9.5244 9.1594 9.0938 10.0535 9.3252 9.0485 9.11005 8.94065 8.67935 10.1565 9.26395 8.7944 8.9099 9.065 9.0237 8.8787 8.4071 9.1989 9.3851 8.9809 9.1403 9.2635 9.08 9.0036 8.7091 8.8984 9.7849 9.3462 10.291 9.6008 9.6451 9.9566 9.6231 8.6927 9.6321 8.9184 8.7374 8.3582 9.717 8.4802 8.6807 8.6469 8.9707 10.325 9.7148 9.6593 10.063 9.6768 9.3066 8.9091 9.1022 8.7159 11.078 10.942 9.2466 9.3204 10.827 9.3963 9.1839 9.63426 9.63284 9.44108 9.77082 9.1827 9.1145 9.1843 8.8794 9.5172 8.8477 9.48 9.5761 9.8036 9.2516 9.1211 9.3997 9.2277 9.2695 9.5667 9.6794 9.6956 10.003 9.0704 10.033 10.781 10.076 10.412 9.5463 10.194 9.3059 10.416 9.2911 9.088 10.738 10.048 9.0644 10.764 10.207 9.7042 9.3694 10.554 10.203 9.5412 9.0928 10.37 9.8545 9.4711 9.6558 10.246 9.4096667 10.7576667 8.2273667 8.3298 8.2265667 8.2411 8.3047333 10.555 9.6928 8.7272 8.5432 8.8041 8.0972 7.671 8.4899 8.2615 9.4943 9.3945 9.1355 9.4797 8.2558667 8.4996 8.7625667 8.5656667 8.3010667 7.9602 8.3053667 8.4847667 8.1506 8.1511667 9.9142 9.6345 9.1179 10.586 9.7877 10.294 10.197 10.466 9.4736 8.9914 9.1032 10.257 9.7765 10.171 10.372 10.168 10.05 10.142 10.069 9.4074 9.5407 9.9119 10.644 10.394 9.8307 9.807 9.5177 9.619 9.3261 9.1326667 9.7255 10.1816667 10.6195 9.32385 9.37905 10.3197 10.4843333 8.98155 9.7714 10.37 10.523 9.0808 9.6586333 10.2174333 10.379 8.2558667 8.4996 8.7625667 8.3053667 8.4847667 8.53432 8.3010667 7.9602 8.1506 8.1511667 9.9819 9.8507 9.7705 9.6416 9.4769 8.5426 9.8165 8.5573 9.7368 8.6832 8.2774 8.3628 10.24 10.269 9.687 9.183 9.9908 9.7734 9.2391 10.287 9.4846 9.2821 8.4343 8.5051 8.4557 9.7105 10.317 10.558 10.643 10.553 10.43 10.059 10.051 8.9348 9.0797 8.9155 10.482 8.9519 10.516 9.14605 10.7425 8.91185 10.625 8.9244 10.5005 9.04165 10.5855 9.0194 10.379 10.532 9.2199 9.6094 9.2989 9.0267 8.6400667 9.2972 8.81675 8.86075 8.7193 9.0614667 9.349 8.5917 9.0867 9.6773333 9.5557333 9.5089667 8.9879 9.5238 9.4437 9.7097667 10.764 10.388 10.799 9.3175 9.7801 10.489 9.9935 9.8506 8.9843 8.5736 10.109 9.8135 10.187 9.2727 8.7606 9.4597 10.001 9.8471 9.7563 9.32 9.2882 9.4226 9.1119 8.5301 8.5677 9.9012 9.3318 9.4442 9.3055 9.3681 9.1726 9.3086 9.2848 9.1629 9.183 9.99025 9.04155 8.90585 9.3582 8.967 8.9864 8.7295 8.9297 9.3777 8.3525 8.1504 8.9795 8.2631 +"G27" 9.0621 9.9110333 9.61635 10.6875 10.4025 10.922 9.6337333 11.6375 10.633 11.0845 9.77365 12 10.943 9.3842 10.409 9.02695 11.7953333 10.1016 11.033 10.601 11.056 12.1956667 11.2285 11.573 10.227 9.8425667 10.5138 10.54 11.1682 10.239 12.232 10.424 10.088 10.2546667 9.0872 9.2209333 11.4975 10.1776 10.2372 10.249 11.4465 10.551 11.3656667 10.906 12.3273333 12.186 10.3513333 10.409 10.364 10.418 11.606 10.6665 10.82725 9.1638667 11.997 10.629 10.2035 10.416 11.6743333 9.6367 10.6626667 12.186 10.812 10.6105 10.359 11.321 11.606 10.102 10.507 11.33 10.1695 10.1605 10.3166667 10.7226 10.10974 10.833 11.698 11.2312 10.043 11.919 10.483 10.289 10.4493333 9.9747667 10.359 10.4526667 10.4273333 10.0873333 9.3758 9.8543 11.818 11.829 10.556 11.424 10.438 10.6373333 10.153 11.676 11.316 11.728 11.3 10.715 10.575 10.205 10.508 9.45 11.455 10.982 10.176 11.282 9.576 10.137 11.394 11.105 10.829 10.866 9.4178 10.694 10.11 9.9422 9.5574667 9.1254 10.0871667 11.6683333 11.489 10.69975 9.6764 9.8006 10.713 8.9563 9.9248 10.324 11.667 10.716 9.6498 10.581 10.5896667 10.959 10.9123333 10.7023333 10.7286667 9.6075 11.589 11.1493333 10.292 10.187 10.651 9.8727 11.152 8.1134 10.279 10.24 10.239 9.5436 10.59 9.7527 11.6463333 9.9784 10.612 10.573 10.0784 10.662 9.97765 10.268 9.7576 10.468 10.1274 9.8577333 10.461 9.9829667 10.9143333 10.3307 10.866 11.848 11.339 10.111 10.57 10.25 10.322 10.707 12.1735 12.074 12.226 12.159 11.815 10.9815 11.1925 11.6025 11.8255 11.8755 11.525 11.149 11.66 11.8 9.7428 9.6079 9.9988 10.197 11 11.157 12.069 11.444 10.081 9.8819 10.306 10.307 10.046 9.7137 9.4885 9.0984 10.003 9.6854 10.318 10.213 10.058 10.056 10.298 10.303 9.926 10.293 9.2334 9.9975 10.65 11.834 12.04 9.9113 10.342 10.288 10.786 10.115 10.354 11.07 9.7183 11.129 10.433 10.3772 10.3256 10.9638 10.09386 10.442 10.396 10.329 10.389 10.073 10.156 11.425 11.516 10.501 10.26 10.043 10.338 10.297 11.033 11.45 11.589 11.604 11.668 10.285 10.061 11.002 10.3 10.373 9.7494 10.055 9.7014 10.221 9.6366 10.23 10.779 10.241 10.1 10.605 10.06 11.058 10.521 10.509 10.198 10.441 10.378 10.29 10.056 10.044 10.259 10.1425 10.1565333 10.1 8.8496333 9.1557 9.1338333 8.884 8.7735333 10.778 10.94 11.248 11.618 11.557 11.184 11.213 9.3842 10.249 11.5 9.5686 10.767 9.4288 10.3913333 10.7756667 11.508 10.6216667 10.7523333 9.1241333 11.1363333 11.5413333 9.1171667 9.0162 10.222 10.536 9.7781 10.266 10.086 10.292 10.552 10.533 10.654 11.48 11.61 10.306 9.9697 10.374 10.34 10.194 11.175 11.51 11.5 11.231 11.646 12.198 11.547 11.616 10.672 11.328 11.834 11.505 12.201 9.8402 9.9895 10.3773333 10.156175 9.8922 9.94255 10.566 10.3026667 9.855 9.8481 10.1645 9.8897667 9.9113667 9.9461 10.4525 10.59975 10.3913333 10.7756667 11.508 11.1363333 11.5413333 10.4912 10.7523333 9.1241333 9.1171667 9.0162 11.443 10.868 10.219 6.5362 11.291 10.716 11.535 12.201 10.402 10.706 10.317 10.447 9.1751 9.8108 8.9515 10.292 9.7047 9.9916 10.544 10.999 11.106 12.242 8.7701 8.2144 8.5558 10.284 11.13 11.986 10.484 10.278 10.26 11.348 10.259 10.093 10.53 11.175 10.1535 10.4155 11.2075 10.5335 10.325 10.3125 11.4755 10.74 10.437 10.4475 10.7815 10.2995 10.959 11.997 11.606 11.118 11.631 12.172 10.144 11.426 11.7115 12.264 11.7785 10.217 11.6765 11.462 10.042 9.8485 10.879 11.367 9.9523333 10.5948333 10.6763333 11.5703333 11.613 10.287 11.17 11.422 11.974 10.49 10.283 10.249 10.515 10.632 11.071 10.344 10.416 10.403 11.014 11.004 10.48 10.83 10.518 10.543 10.418 10.275 10.018 9.1732 9.3181 10.566 10.539 10.659 10.515 10.347 10.382 9.4182 9.9779 10.521 10.4485 10.5545 9.81895 9.7304 10.244 10.32 10.554 11.487 11.555 10.203 10.669 10.541 9.8257 10.197 +"G28" 10.5405 10.5713333 9.75265 11.034 9.60815 11.183 10.6003333 10.936 9.2359 11.097 10.1557 10.689 10.0264 10.848 10.277 9.83085 10.8766667 10.776 11.457 11.38 10.53495 11.227 10.3885 11.826 10.963 11.06 11.207 11.282 10.33012 10.804 11.091 10.673 10.552 11.066 9.7178 10.2558667 8.9728 9.00535 10.9916 10.122 9.23945 10.739 10.0580333 9.0976 9.8611667 10.839 10.0677333 9.58 10.152 9.2796667 9.092 10.9825 11.094375 9.9472 10.375 10.4480333 9.89085 9.9602 9.544 9.6361 9.9725 10.883 11.805 9.6096 9.7981667 10.0116 10.375 10.371 11.283 7.6765 10.938 8.8176 11.402 10.36668 10.9362 11.47 9.41682 9.95778 11.2595 10.615 10.322 10.395 11.0906667 11.038 10.6843333 10.4786667 10.1349333 10.4146667 11.913 10.1581333 10.84 8.5271 9.2109 10.2341 8.9335 11.1163333 9.3402 11.226 10.675 8.7897 10.363 9.9121 8.8848 10.19 9.1746 10.705 10.375 10.207 11.422 11.168 8.4665 11.179 10.778 10.676 10.672 9.778 10.972 11.353 10.59 10.446 10.9 11.097 10.5743333 11.18 10.882 11.1435 8.4966 10.907 10.732 10.255 10.824 11.264 10.156 9.2724 11.289 10.895 10.3589667 10.8663333 10.2873 10.2421333 10.7541333 10.5082667 11.283 9.8003333 11.676 8.7554 11.676 11.0765 8.8848 10.137 10.618 8.9277 10.07 11.327 9.4226 8.9335 10.5896667 9.2497 11.338 10.921 9.381 9.9027 9.82345 10.873 11.111 9.95135 10.8033333 10.3554667 10.2169 10.2949 10.0279667 11.0383333 10.285 11.293 10.968 10.984 10.978 11.327 10.612 9.5196 9.757 10.7118 9.8119 10.8 9.5655 10.55785 11.078 10.5192 9.96745 11.0365 11.412 10.405 11.895 9.1027 10.984 8.905 10.45 11.112 11.147 10.795 10.501 10.839 10.822 10.589 10.445 8.9448 10.983 9.259 10.44 9.3005 10.52 9.3005 10.574 10.907 10.589 11.024 10.875 10.878 10.969 11.424 10.562 11.612 10.055 9.2497 10.314 11.223 9.9711 10.703 9.4261 9.5944 11.39 8.5513 10.115 11.31 11.612 10.6874 10.21254 10.77228 10.7838 10.932 11.396 11.22 10.432 10.73 11.125 10.646 11.281 10.277 11.457 10.4 10.191 8.7554 10.414 10.486 10.128 10.445 11.321 11.396 10.552 11.233 11.197 10.984 10.138 10.093 8.8997 8.8404 9.5833 10.177 11.074 8.8848 10.762 11.686 8.8404 10.869 10.566 8.8404 8.9277 10.394 11.043 11.836 10.77 11.234 9.7889 9.7246667 10.1496333 10.2706 10.3705333 10.997 10.6793333 10.659 9.7117667 10.459 10.891 10.689 10.618 10.907 10.717 10.506 11.649 10.319 10.145 11.027 11.649 11.317 10.3533333 9.9401667 10.7353333 9.7864 10.5502333 10.837 9.9716667 9.8047667 10.0206667 10.6174 10.095 10.813 11.234 8.4118 8.8852 11.009 11.384 10.807 8.433 10.387 10.839 10.255 10.778 8.8404 10.375 10.965 10.33 9.2758 10.871 10.763 10.045 11.742 10.141 10.672 11.185 9.2349 8.7337 9.185 11.317 10.7386667 10.6875 10.6111333 10.87975 10.16085 9.87275 10.8035 10.4683333 11.0475 9.733 10.905 10.0910667 10.8606667 10.0346667 11.0993333 10.124725 10.6091667 9.8876667 10.6078 10.3956333 11.2923333 11.0272 10.2091333 10.8076667 10.827 10.8423333 10.255 10.968 11.287 10.682 9.259 10.783 10.038 11.309 11.168 11.223 11.043 10.883 10.977 10.898 9.7422 10.562 8.7532 10.336 10.934 10.84 9.9445 10.619 11.091 9.1746 10.314 10.828 9.259 8.5545 11.394 9.5426 11.895 10.782 10.123 9.37 11.317 10.793 10.718 10.0973 8.8305 10.5675 9.8131 10.809 10.07535 10.8735 10.725 10.466 11.266 11.137 11.203 10.861 10.338 11.096 11.256 10.215 11.1276667 11.5015 10.507 11.2655 11.0105 9.6308333 9.44425 8.2479 10.2943333 9.7663333 10.3368333 10.1105 8.8736667 11.5016667 10.5002333 11.09 10.258 8.4665 10.556 10.641 10.926 10.876 10.755 11.125 8.8848 11 11.161 10.338 11.649 9.3176 10.969 10.856 11.321 11.323 10.711 8.8422 9.4208 9.0078 11.398 9.8888 11.062 11.236 9.4166 11.338 8.4308 10.977 10.469 9.4208 8.1269 11.411 11.011 10.507 9.9662 10.691 10.44 10.389 8.7897 10.535 10.718 10.615 9.185 10.137 11.341 11.396 +"G29" 10.4405 9.3109 8.92735 9.06565 9.5352 8.0853 9.2021 10.0342 9.8511667 9.7494 9.0989 8.6717 9.22005 7.6062 8.649 9.8852 7.1717667 9.3964 9.236 9.4909 9.86635 8.4731 9.4718 7.8678 10.298 9.3040667 9.20686 10.218 9.84966 9.96855 9.7761 7.7747 10.419 10.8836667 10.56 8.8277 9.30705 9.2756 9.72398 7.7299 9.3196 10.454 8.2741333 9.9404 9.0663667 9.4609 9.7476667 10.647 10.906 10.8 8.406 10.744 11.15125 8.9455333 9.5057 10.5183333 10.2195 10.295 10.8383333 9.5821 8.2989667 9.4609 8.3945 9.6127 9.6661 10.9733333 8.406 9.5723 7.208 10.07 9.02965 9.4255 9.5004333 9.21428 9.36558 9.9383 9.79794 10.05902 8.62935 10.137 8.0658 10.529 10.8496667 10.679 10.852 10.8756667 10.959 10.689 9 9.2215333 9.4072 8.1001 9.44255 10.4506667 9.8296 9.6289 9.7688 9.0552 9.5541 9.843 9.4068 10.0895 10.448 10.491 10.585 8.0083333 10.447 10.178 10.498 10.088 10.02 10.451 9.9224 10.09 9.8814 10.265 9.9251 10.444 9.7383 9.2748 9.4818333 9.305 9.7154 9.5862667 8.6794 10.3885 11.432 11.274 10.858 10.738 10.124 10.363 8.0592 10.454 9.28695 10.935 10.9916667 11.1926667 10.9843333 10.931 10.9436667 8.8715333 9.0445 10.3585667 10.368 9.8197 10.394 10.0318 10.316 9.278 11.012 10.763 10.846 10.039 10.49 9.6184 9.8881667 8.0925 8.8466 9.7984 9.66605 10.3555 9.3492 10.2865 9.98455 10.09565 9.9028667 10.2563333 10.2886667 10.5563333 10.3813333 9.9326 8.7607 8.473 8.76845 9.4789 10.074 8.5329 9.7586 9.7448 10.2145 10.10345 9.79575 9.7549 8.612 10.353 9.90775 9.3691 9.15295 9.03945 9.791 8.9549 9.0163 8.8405 8.2373 9.189 7.8431 9.9574 9.7703 9.1428 9.1496 9.3219 9.3799 9.0664 9.3782 9.2498 9.5739 9.1096 9.0074 8.3178 9.7266 9.4637 9.6704 9.4754 9.3523 9.429 9.5059 9.1567 9.3803 10.356 9.0006 9.2375 9.4386 8.4427 8.2676 8.3002 8.4541 8.928 9.9622 10.684 10.201 9.7679 10.693 9.8289 10.21 9.59272 10.11356 10.6682 9.87618 9.5711 9.7495 9.6434 10.476 9.9798 9.7735 10.49 9.9301 9.729 9.4764 9.2192 9.9473 10.266 9.9169 10.637 10.163 9.8949 8.6945 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7.9302 7.0722 7.2330667 6.8252333 7.7601 8.2877 8.2942 6.6979 7.97595 8.34785 7.4544667 7.1426 6.82082 6.4549 7.49218 7.8424 7.66125 7.008 8.0762 6.6735 7.4728 7.5365667 8.0294333 7.7569 7.7399333 7.8444333 7.6369 6.4649667 8.5634 8.0448 7.1654 8.4913333 7.3971 9.2954667 7.5201 5.9997 7.7786 7.6897 6.571 7.632 6.6846 6.7052 6.8718 7.1937667 6.8067 6.6063 6.473 6.6234 6.5895 6.4562 6.5951 7.0042 6.8185 6.5891 6.7213 6.6026 8.3361 6.7084 6.8227 7.0648333 7.6031 7.0962 7.745 7.61735 6.9301 7.5266 7.1323 7.6516 6.901 6.5241 7.6987 6.4892 6.7065 6.6635 7.5099333 7.7517667 8.2781667 6.9243333 6.6601667 6.6426 7.4188 6.6207333 6.5954 6.5021 6.7096 6.68 8.6705 8.1372 9.3613 8.104 6.8222 6.6356 6.703 6.5615 6.5923 7.8821 7.4948 7.1082 6.98955 6.8227 6.8892 6.69775 6.85905 6.82925 6.9658 7.0901667 7.2170333 6.8574667 6.8563333 6.7963 7.9364 8.1638 7.7985 7.8787 8.7634 7.1493 6.88 6.70945 7.50485 7.53805 7.4779 8.04455 7.54795 6.60195 6.939 6.92395 6.76975 6.8389 7.7632 7.0434 7.4609 7.7565 7.005 6.9627 6.9155 6.8679 7.679 7.0191 7.4921 7.6115 6.1531 6.4394 6.3191 6.2169 6.5067 6.345 6.6341 6.783 6.6957 6.7518 6.8045 6.6433 6.2081 6.5221 6.4347 6.6803 6.5026 6.6007 6.8134 6.9945 6.5988 7.3674 8.0345 7.9228 8.6883 9.4462 6.6201 6.636 7.9731 7.8336 6.8206 7.934 8.123 6.72976 6.74362 7.76784 8.02766 6.5233 6.8075 6.7409 6.2393 6.4214 6.438 6.3312 6.556 6.8139 6.8185 6.5313 6.3682 6.3881 6.6079 6.3709 6.4918 6.668 6.8142 6.7703 6.567 6.5091 6.5854 6.4496 6.3344 6.6635 6.5193 6.7284 6.7758 6.6555 6.7163 6.6003 6.6576 6.7143 6.4012 6.9803 7.2819 6.6471 6.5266 6.9489 7.8045 6.5095 6.6611 6.591 6.8535 7.1977667 7.9424333 6.5186 7.7543 6.8216 6.8018333 6.6681667 6.6344667 6.5525 8.0591 8.6301 9.0593 9.2766 10.313 10.761 7.2378 8.3553 8.1967 7.6077 8.0175 8.0882 7.6550667 7.7526333 7.4768333 7.5157333 7.0202667 7.4741667 7.4388667 7.5986 7.8308333 7.5106333 6.9634 7.3395 7.0781 6.8287 6.7175 6.9169 6.8572 6.7589 7.1968 7.2879 7.5748 6.6026 6.5513 6.752 6.2858 6.2633 6.326 6.3958 6.5588 7.3137 7.0515 7.1208 6.7371 6.7524 6.7134 6.514 7.5414 7.1535 7.98 6.9571333 6.9104 7.0380333 7.01225 6.91475 6.8558 6.83855 7.1121667 6.79625 6.85205 7.0191 6.9488 6.9436 6.9318667 6.9859 7.118625 7.6550667 7.7526333 7.4768333 7.4388667 7.5986 7.48382 7.0202667 7.4741667 7.8308333 7.5106333 8.5955 7.1226 7.2064 8.7362 8.1909 6.9664 8.5387 8.0499 6.3371 6.4824 6.3231 6.4881 6.4471 6.8089 6.8597 7.4826 6.6836 6.8423 7.3002 6.6114 7.2372 8.4691 6.5878 6.8614 6.6134 8.2102 8.8425 8.4898 6.8572 6.3173 6.4368 6.4013 6.3758 7.4616 7.7017 7.5595 6.92575 6.86635 7.1425 6.77615 6.5734 6.8445 6.98005 7.14195 6.7921 6.89975 7.40775 6.8839 7.0644 7.2995 7.7601 7.5416 8.0268 7.3645 7.7236667 8.0872 9.39805 8.37055 8.2024 7.6746333 9.25985 8.3539 6.7906667 7.0671 7.6838 7.9131667 6.8884667 7.3827667 8.1776 8.1263333 7.187 8.0756 7.473 8.0815 7.2269 7.2533 7.6203 7.5157 7.0788 7.3796 6.6577 6.8694 7.4323 7.0985 9.1283 7.6663 8.2032 7.5382 7.1858 7.7964 7.295 7.1175 7.2053 7.247 6.6524 6.5063 6.6731 7.6352 6.9211 6.8035 6.736 6.6241 6.8567 7.0809 6.71365 6.4844 6.61415 6.72595 6.6977 7.3973 8.1891 7.2075 8.9941 6.6103 7.058 8.4699 6.8732 7.6813 +"G42" 10.608 11.0023333 9.9246 10.7665 10.03935 10.122 10.6773333 10.4935 11.0573333 10.281 9.96505 11.141 9.9492 9.2987 9.7696 10.493 11.4823333 9.93575 10.128 10.3285 11.547 10.9633333 10.0203 10.636 9.7094 9.2895 9.98692 10.044 10.3594 9.5551 9.6821 9.0805 10.106 11.0103333 10.562 10.3386667 10.1807 9.3171 10.4164 9.5119 10.7545 9.9043 9.8688667 9.7849 10.9483333 10.433 10.2956667 11.513 11.264 10.294 9.877 10.686 11.00825 9.9967667 9.4233 10.901 10.2445 10.044 10.214 10.187 9.2344333 10.433 10.124 9.87145 10.0575 10.2243333 9.877 10.84 9.3184 10.371 10.0905 10.471 9.6161667 10.562 10.11728 9.8001 10.5788 10.5664 8.74625 10.471 9.4271 9.9627 11.2666667 10.845 11.116 11.0696667 11.391 11.0053333 9.5355 10.248 10.121 9.9206 7.9259 11.692 10.585 10.909 10.68 10.193 10.821 10.896 9.5266 10.5165 9.9759 9.6711 9.9602 9.1748333 10.276 9.2357 10.088 9.606 9.7596 9.9832 9.8406 9.8829 9.7171 10.12 9.9401 10.195 9.8926 10.171 9.5013333 9.4510667 10.259 9.8382667 9.6677 10.541 10.413 11.22 10.922 9.8996 10.325 10.621 9.6035 8.0306 10.07865 10.7286667 11.0533333 11.2606667 11.448 10.9926667 10.8433333 9.5816 10.397 10.3126667 10.273 10.474 10.451 10.1115 10.19 10.364 11.953 11.217 11.264 10.248 10.589 10.122 10.5676667 9.3599 10.281 9.81155 9.8043 10.102 10.03395 10.3725 10.507 10.0029 9.9380667 10.002 9.9935667 9.9822667 9.5227 9.7718 9.6539 10.197 9.72965 10.938 11.112 9.1189 9.7085 10.1955 10.9625 10.925 11.057 11.1255 10.453 10.1075 10.1455 10.291 10.399 10.408 10.571 9.7402 10.108 9.9374 9.386 9.4611 9.1439 9.7773 10.357 9.6949 10.353 9.6656 10.446 10.252 10.167 10.19 10.236 10.196 10.319 10.407 10.218 10.302 10.897 11.06 10.212 10.574 11.068 11.139 9.918 10.097 9.8396 10.241 9.5872 9.3875 9.3287 10.236 10.164 9.8491 10.415 10.172 11.19 11.798 10.13 11.279 11.224 10.03158 10.09214 10.37014 10.5562 10.293 10.308 10.104 10.584 10.269 10.708 10.956 10.766 9.7426 10.19 10.163 10.315 10.793 10.983 10.862 10.565 10.362 9.8066 10.188 10.045 9.9347 10.257 10.032 10.118 10.149 10.072 10.232 10.034 10.355 10.3 10.105 10.179 10.179 10.153 9.9563 10.818 10.216 10.121 10.225 10.87 10.054 10.186 10.189 10.095 10.4046667 10.9056667 10.5583333 10.126 9.5601 9.3314 9.0538 9.0108 10.17 9.7748 9.8203 9.985 10.353 11.647 12.613 9.2987 9.5119 9.7449 9.4871 9.629 9.314 10.9683333 10.7083333 11.0663333 11.221 10.6636667 9.9525 11.132 11.3223333 10.34 9.8204 9.0293 9.3262 8.9864 10.118 10.484 10.674 10.657 10.189 9.6449 9.6149 9.6404 10.372 10.344 9.966 10.039 10.114 10.244 10.069 10.072 9.0323 9.208 9.4422 10.226 10.643 10.456 10.007 9.7186 10.287 9.3873 9.8914667 10.04415 9.8910333 9.8385 9.9655 9.96885 9.9131 9.7957667 10.00415 9.967 9.7893 9.8405333 10.138 10.1643333 9.9835667 10.077325 10.9683333 10.7083333 11.0663333 11.132 11.3223333 11.151 10.6636667 9.9525 10.34 9.8204 11.499 10.471 10.28 8.6898 9.9281 10.331 10.244 8.7132 10.016 10.787 10.717 10.894 9.6646 9.9205 9.8454 10.648 9.6686 9.5938 10.095 10.017 9.9142 9.6811 8.9805 8.723 9.036 9.986 9.6528 10.13 9.5704 9.6612 9.6598 9.4711 9.8274 10.057 10.013 9.6513 10.2525 10.8955 10.4285 10.7205 10.263 10.6775 10.4775 10.6595 10.2085 10.812 10.4625 10.677 10.263 9.4233 9.877 10.271 10.587 10.118 10.5276667 10.5655 10.4545 10.457 9.56005 10.6613333 10.524 10.087 9.731 9.9610333 10.4816667 10.8883333 9.8709 9.8819 10.6263333 10.9896667 10.176 10.321 10.364 10.366 10.497 10.101 10.505 10.618 10.6 10.148 10.025 10.286 10.451 10.25 11.765 10.435 9.988 10.19 10.195 10.226 10.565 10.47 10.605 10.615 9.9747 10.21 10.419 10.488 10.755 10.349 10.181 9.9041 10.448 10.631 10.2625 10.1835 10.469 10.4345 10.563 11.038 11.423 11.115 10.362 10.454 9.8133 10.608 10.251 10.514 +"G43" 7.87525 8.2466333 8.55005 8.7956 8.58 8.5604 7.9782 7.8683 7.9092333 8.25785 8.85155 8.6332 8.74005 8.5912 8.8782 8.3275 8.5006333 8.6714 8.3512 8.2509 8.453 8.6669 8.54555 8.0009 8.1504 8.5475 8.79666 8.1548 8.37598 7.6729 8.4738 8.4417 8.51 8.0038 8.6075 8.495 8.52385 7.86005 8.55438 7.7777 9.10735 8.1729 7.9672667 8.0946 8.642 8.3433 8.5827333 8.3953 8.0261 8.4492667 8.5392 8.1299 7.980825 8.2458667 7.9339 8.331 8.21405 8.8926 8.7706667 8.8486 9.138 8.3433 8.8518 8.513 8.4165333 8.3079 8.5392 8.3423 8.4741 8.4849 8.9209 8.88115 8.4792 8.44348 8.73562 8.3505 8.44932 8.85858 8.06555 8.2885 8.2159 8.5462 8.1828 8.1009 7.9199333 8.0049667 7.9946333 8.1853 8.8027 8.6816667 8.6188 8.6778 8.12045 8.3704333 8.6155 8.5286333 8.8169 8.3466 8.957 8.5702 9.1105 9.46125 8.3319 7.6935 8.1702 7.7687333 8.4688 8.2698 8.5597 8.3439 8.488 8.3626 8.5139 8.4095 8.0932 8.3488 8.3407 8.4394 8.1037 8.7938 8.8142667 8.4516 8.717 8.4152333 8.7978 8.39335 8.1603 8.3858 8.2066 7.7705 8.462 8.3552 8.1935 8.2317 8.7388 7.9703667 8.0375 7.9591 7.8841333 8.0412667 8.1799667 8.2241667 8.3142 8.6105667 8.4415 8.6617 8.3898 8.6776 8.3993 8.8059 8.0331 8.4408 8.4485 8.7139 8.6445 9.0406 8.4357 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8.6429 8.8151 8.3408 8.30425 8.51145 8.47495 8.54595 8.5199 8.70185 8.58465 8.7634 8.6437 8.58155 8.51315 8.1983 7.9081 8.275 8.5397 8.7256 8.4524 8.4508 8.0992 8.3297 8.3845 8.7531 8.8993 8.0637 7.9009 8.004 8.0088 8.1476 8.2937 8.1825 8.2417 8.1175 8.1627 8.177 8.0653 7.9806 8.2601 8.0984 8.1773 8.1805 8.0981 8.1302 8.5532 8.0851 8.1217 8.2575 8.0819 8.5354 8.9279 8.4177 8.2077 8.6846 8.2049 8.5083 8.8111 8.7398 8.17806 8.04108 8.12656 8.04492 8.1585 8.2089 8.11 8.2951 8.4497 8.057 8.2506 8.3408 8.6577 8.1879 8.4594 8.3933 8.3308 8.3593 8.2577 8.2738 8.2537 8.5103 8.0114 8.0004 8.0685 8.2016 7.7596 8.0404 7.9064 8.0095 7.8779 8.1035 8.024 8.1248 8.2715 8.094 8.2478 8.183 8.5682 8.5034 8.2925 8.3197 8.2291 8.4328 7.936 7.9895 8.0523 8.1049 8.2125 8.2532333 8.3363667 8.1990333 8.1908333 8.2131 8.2741667 8.3834333 8.0957 8.2009 8.5208 8.9027 9.6403 10.73 11.506 8.5426 8.0844 8.5634 8.3402 8.2323 8.2363 8.8321667 8.7201667 8.8548 8.4413333 8.1716667 8.6179667 8.6359333 8.7225 8.3403667 9.1877667 8.5914 8.6957 8.706 8.4253 8.1974 7.8969 7.7432 8.0873 7.706 7.9434 8.0837 8.2559 8.1036 8.3353 8.2855 8.353 8.5438 8.7884 9.3397 8.408 8.2938 8.3959 8.2919 8.057 8.2885 8.2652 8.1729 8.4465 8.0823 8.1328333 8.07015 8.0501667 8.315575 8.1084 8.08475 8.07765 8.104 8.14015 8.1272 8.21605 8.1337333 8.1108667 7.9868667 8.1455 8.279375 8.8321667 8.7201667 8.8548 8.6359333 8.7225 8.40322 8.1716667 8.6179667 8.3403667 9.1877667 7.9638 7.9479 8.3956 7.8987 7.9736 8.4933 8.2237 8.6075 8.0134 8.1831 8.3535 8.2212 8.2252 8.1127 8.4924 8.5485 8.5501 8.4263 8.3689 8.4106 8.2583 7.9079 8.3471 8.6672 8.7944 8.2837 8.5287 8.7093 8.3559 8.3073 8.0907 8.1655 8.1963 7.9628 8.2363 7.8715 8.15045 7.8935 7.814 8.1048 8.1107 7.99595 7.81025 8.07405 8.24205 8.21265 7.9788 7.8399 8.5663 8.0371 8.2256 8.186 8.258 8.1698 8.5591333 8.91915 9.13505 9.30255 8.82325 8.6065 9.03705 9.1115 8.2357667 8.4680667 8.4422 8.7022 8.1863 8.2866 8.5643333 8.4871667 8.0471 8.3489 8.4535 8.3967 8.3484 8.0649 8.1767 8.2516 8.2858 8.3612 8.1228 8.0511 8.2313 8.2996 8.6093 7.9332 8.0345 8.0019 7.98 7.9637 8.017 8.1902 8.0477 8.5239 8.6601 7.9576 7.9836 7.9373 8.0524 8.0919 8.2917 8.161 8.5769 8.2086 8.2236 8.08715 8.3195 8.3444 8.2691 8.189 8.3939 8.2754 8.4015 8.2652 7.9648 8.3603 8.1679 8.1319 +"G55" 11.6145 11.8906667 11.595 11.065 11.357 10.764 11.649 11.132 11.7536667 10.8815 11.563 11.67 11.269 9.6808 10.938 11.3205 9.3057333 11.56 10.633 11.0445 11.1055 11.57 11.5795 9.6492 11.657 11.076 10.9988 11.956 11.2686 10.7275 11.515 10.25 11.892 11.6613333 12.014 10.786 10.23445 12.4075 11.556 9.5235 11.366 11.948 9.7192667 12.147 9.6668333 10.493 11.3536667 12.25 12.298 11.306 10.746 12.002 11.8055 10.4936667 11.286 11.6323333 11.79 11.722 12.721 11.9 9.4956667 10.493 11.177 11.422 12.9553333 12.0973333 10.746 11.857 9.289 11.813 11.3995 11.6925 11.3043333 11.3486 11.3342 11.57 11.513 11.2852 10.05425 11.551 10.09 11.89 11.7493333 11.564 11.623 11.772 11.834 11.5753333 10.736 11.1303333 9.8496 9.6038 12.335 8.7396667 11.88 11.836 11.895 9.9196 10.89 11.115 11.392 11.5495 11.959 11.94 12.053 9.7445667 11.687 11.944 12.066 12.001 11.964 12.213 12.089 11.808 11.874 11.633 11.802 11.857 11.794 11.858 13.0946667 11.7256667 11.3886667 11.3586667 9.3937 11.53725 12.361 12.004 11.503 11.294 11.189 11.598 9.8877 12.124 11.656 11.786 11.5536667 11.598 11.6476667 11.7476667 11.786 10.5689667 10.202 11.5396667 11.945 11.682 12.143 12.183 11.742 12.049 11.824 12.049 12.311 11.832 11.875 11.671 11.524 9.4569 10.153 11.4545 11.648 11.8045 11.7415 12.09 12.163 12.3345 12.7326667 12.8373333 12.5466667 12.2543333 11.786 11.526 9.5693 9.9912 10.8345 12.241 11.761 10.542 12.121 11.5645 11.15 10.705 10.77 10.3555 9.21045 11.7705 11.469 11.0595 10.8295 10.653 11.494 10.939 9.4102 9.7845 10.693 11.992 10.338 11.945 11.497 10.819 9.4825 9.6268 12.066 11.731 12.167 11.833 11.986 12.078 12.177 12.218 11.839 11.887 12 12.208 12.001 11.722 11.907 11.913 10.763 11.41 11.607 11.559 11.455 11.206 11.261 10.945 10.474 10.257 12.17 12.004 11.84 11.457 11.974 11.384 11.866 11.502 11.7004 12.026 11.5906 10.948 10.954 10.912 11.496 11.59 11.813 11.864 11.684 11.613 11.042 10.927 11.621 11.944 11.633 11.955 11.823 11.749 11.427 10.651 11.771 11.891 12.023 11.827 12.311 11.791 12.008 11.808 11.906 10.874 11.604 11.781 10.8 11.727 11.72 11.93 11.69 11.733 11.863 12.059 11.807 11.678 11.644 11.476 11.508 11.751 11.8096667 11.706 11.5356667 11.6803333 11.5676667 11.3333333 11.2193333 11.411 11.054 10.619 10.386 11.458 11.285 11.079 9.6808 9.5235 9.6553 10.748 10.858 10.105 9.5576 9.6318333 9.6042333 10.3466667 10.5003333 9.1632667 9.5427333 9.4082333 8.9484 9.1815667 10.978 9.94 9.674 11.115 11.19 11.574 11.721 11.285 11.381 11.421 10.901 11.991 11.45 11.57 11.827 11.575 11.703 11.885 11.912 11.076 11.538 11.311 11.772 11.829 11.567 11.251 11.687 11.392 11.601 10.814 10.8825 11.4396667 11.32675 10.9395 10.974 11.54 11.3286667 10.763 10.816 11.513 11.2676667 10.941 11.0663333 11.587 11.39775 9.5576 9.6318333 9.6042333 9.5427333 9.4082333 10.3528 10.5003333 9.1632667 8.9484 9.1815667 11.187 11.606 12.137 11.074 11.4 12.319 11.009 9.4892 11.653 11.445 11.655 11.995 11.745 11.744 11.791 11.481 11.655 11.642 11.605 11.88 11.656 10.948 10.966 11.461 11.531 11.365 11.234 11.355 11.627 11.686 11.63 11.572 11.727 11.676 10.711 11.389 11.606 11.323 11.832 11.2615 11.831 11.234 11.9 10.902 11.5765 11.2145 11.7835 11.1735 11.248 11.286 10.746 11.807 11.157 10.428 11.3723333 11.451 10.2155 9.48205 9.31905 11.78 10.459 9.661 11.4953333 11.5976667 11.3776667 10.8313333 10.4133333 11.61 11.8123333 10.604 11.897 11.89 12.358 11.353 11.074 11.689 12.017 11.853 11.402 11.525 11.716 11.843 11.735 11.927 11.365 11.204 11.751 11.747 12.072 11.884 11.862 11.89 12.03 11.724 11.124 11.823 11.684 11.992 11.837 11.805 10.673 11.021 11.159 11.331 10.901 11.5505 11.4525 11.7715 11.42 13.266 12.327 13.213 12.843 11.271 11.602 11.418 11.547 10.858 +"G56" 4.55675 4.9940667 4.8492 4.3281 4.59955 5.6695 4.7078667 4.3614 4.5417 4.1885 4.97265 5.7137 4.4108 6.2866 5.4342 4.6717 4.7838667 4.68335 5.2204 4.3981 4.37875 4.8831 4.45695 5.6052 4.7517 4.2130333 4.56652 4.5085 4.54336 4.64415 4.3247 6.259 4.444 4.2446667 4.3096 5.2611333 4.7686 4.4085 4.52732 7.0513 4.4826 4.5806 5.7755333 4.5769 4.7920333 5.0448 4.2336 4.5311 4.2445 4.4518667 5.9596 4.33505 4.5045 5.3885 5.8741 4.4636667 4.96445 4.8087 4.4649 4.7552 4.9035333 5.0448 4.7364 6.2093 4.4634667 4.5317333 5.9596 4.7317 6.2343 4.108 4.3691 4.52475 4.3974333 4.33336 4.3199 4.3572 4.34112 4.38386 6.0848 4.2631 5.6335 4.7129 4.2967333 4.2109333 4.2543667 4.2986333 4.2989333 4.3272667 4.5143 4.5105667 5.9978 6.5363 4.44295 4.3451333 4.1159 4.8693667 4.0227 5.3587 4.417 4.0734 4.6892 4.6079 4.7046 4.5576 4.5941 6.2029 4.5575 5.0319 4.6723 4.965 4.6504 4.5965 4.9386 4.8218 4.6887 4.4061 4.5157 4.3926 4.4735 4.529 4.5985333 5.3871667 4.7294 4.9819 4.5402 4.4544 4.425 4.4107 4.3825 4.7324 4.4488 4.4405 5.6899 4.6316 5.0296 4.4232667 4.2642667 4.2531 4.2711333 4.4701333 4.3372667 5.6497333 5.2843 4.8260333 4.293 4.5047 4.6296 4.36595 4.9413 4.4536 4.2565 4.3818 4.6151 4.7411 5.0367 4.286 4.6226 5.5895 4.6478 6.0113 5.8495 5.81945 4.8684 5.1975 4.64975 4.6954 4.5164333 4.2911333 4.2888667 4.5623 4.6518 4.7770333 5.905 5.6288 5.6786 4.7546 4.7029 4.82 4.9437 4.67345 4.32255 4.44275 4.2173 4.31415 4.9071 4.9191 4.46775 4.1645 4.29555 4.25315 4.3765 4.8956 4.9072 4.9371 4.8642 5.5196 4.9997 4.8552 4.6001 4.7924 4.6718 5.0795 4.6562 4.2378 4.6636 4.2597 4.3413 4.7026 4.5567 4.1044 4.3857 4.0304 4.2128 4.7097 4.2021 4.2766 4.5787 4.5704 4.5034 4.4211 4.5852 4.4101 4.5778 4.3317 4.3227 3.996 4.1255 4.0632 4.5896 4.5276 4.5748 4.3751 4.5766 4.289 4.5044 4.45806 4.65802 4.51418 4.6835 4.622 4.3561 4.68 5.0194 4.372 4.8059 4.2021 4.6886 4.4628 4.4863 4.8374 4.6138 4.7593 4.5355 4.7293 4.4136 4.3091 4.5098 4.3957 4.3036 4.4449 4.3612 4.3818 4.4817 4.3976 4.4865 4.4674 4.4686 4.2983 4.2839 4.3517 4.4307 4.3521 4.3911 4.3381 4.4025 4.365 4.3319 4.4796 4.4914 4.4884 4.5531 4.5199 4.5538 4.4060667 4.3374333 4.8413333 4.7824667 4.7774333 4.8893667 4.9046667 4.7897 4.4311 4.123 3.9592 3.909 4.0136 4.062 4.2163 6.2866 7.0513 5.2987 5.4093 6.6738 6.1309 6.0802667 5.8146667 5.5695667 4.1727333 4.3504667 6.0401 5.7588667 5.8121 6.4609667 6.2915333 4.6821 4.7626 4.7471 4.4831 4.1916 4.3329 4.5199 4.4684 4.391 4.1027 4.2776 4.5107 4.1604 4.3072 4.6253 4.2292 4.766 4.1841 4.5578 4.9882 4.7961 4.872 4.4245 4.3296 4.2061 4.3861 4.438 4.5152 4.2653 4.6689333 4.61205 4.6829667 4.71205 4.87265 4.5331 4.6325 4.6211667 4.62495 4.67685 4.60475 4.7018667 4.6672667 4.4496 4.5963667 4.69065 6.0802667 5.8146667 5.5695667 5.7588667 5.8121 4.1462 4.3504667 6.0401 6.4609667 6.2915333 4.6647 5.0107 4.7018 5.4668 5.2131 4.7811 4.8445 5.6143 4.2148 4.5617 4.4771 4.7404 4.8196 4.7878 5.3278 4.8203 4.9373 4.8198 4.7649 4.7185 4.9316 4.2983 4.8478 5.151 4.8968 4.6092 4.47 4.4463 4.7701 4.448 4.4383 4.4293 4.6094 4.463 4.195 4.411 4.1391 4.3792 4.4438 4.37235 4.33305 4.3011 4.2808 4.65225 4.3309 4.48615 4.3542 4.3542 5.5098 5.8741 5.9596 5.5085 5.5055 5.4012 4.4272333 4.67865 5.723 5.1343 4.9722 4.3989 4.57495 4.4483 4.7755 4.5981667 4.4713 4.5585 4.5081333 4.6670333 4.5032 4.7699333 5.1606 4.4113 5.2692 4.2593 4.3151 4.2484 4.7323 4.8343 4.699 4.2305 4.4038 4.9803 4.8821 4.2122 4.1605 4.2921 4.4304 4.3624 4.504 4.313 4.2869 4.465 4.7235 4.2086 4.2668 4.1303 4.2333 4.1677 4.2446 4.1719 4.6569 4.8875 5.0915 4.472 4.8692 4.7204 4.73725 4.5691 4.3134 4.4769 4.32 4.479 4.0754 4.524 4.5031 4.2295 4.2561 4.0847 +"G57" 8.22245 6.8311333 7.4915 7.99905 8.58895 8.2672 6.4069 7.64975 6.3412333 7.543 7.7032 9.8113 8.3873 7.9383 8.1779 7.7284 7.2414 8.5269 8.4449 7.5266 8.62365 6.9459667 8.77045 7.7901 8.7475 8.3856333 8.1423 8.9157 9.14638 7.59725 7.9845 8.6094 8.6946 8.8487667 7.7088 7.2506667 7.7832 10.1255 8.46292 7.3215 7.82865 8.6458 7.1367 7.8341 8.2782667 8.2101 8.3409667 8.6912 8.7938 8.2081333 7.4456 8.973 8.9795 6.9653 8.2915 8.5196667 8.4562 9.3322 10.1294333 8.7556 7.9520333 8.2101 7.5682 8.13735 9.5334333 7.8134333 7.4456 7.9615 7.9342 8.3775 8.078 8.08175 8.4524 7.88794 7.71616 8.8896 9.18608 9.0015 8.0997 8.7037 7.8282 8.877 8.6748667 8.7185333 8.8750667 8.7339 8.7996667 8.6466333 7.7623 7.8369 8.2319 8.1461 9.89135 8.4035 8.3509 7.9285333 8.42 7.8328 7.8536 7.9102 7.8429 8.12165 8.5485 8.2904 8.6386 7.2103333 8.9064 8.6502 9.1592 8.7898 8.8563 9.1879 8.5695 8.6171 8.6437 8.7422 8.5218 9.0662 9.024 9.1516 9.7131 8.2938667 8.3853333 8.2471 7.8791 8.3634 7.7382 8.0386 8.3256 8.8165 8.708 8.3904 8.2239 9.0898 7.39985 8.8947333 8.6152667 8.7444667 8.7523333 8.8721 8.6506333 7.1439 7.6285 8.6360333 8.3826 8.1171 8.6775 7.7823 9.0252 8.9962 8.8368 9.0779 8.8025 8.1518 8.8049 8.4919 8.4268667 7.9063 7.5357 8.17945 8.08065 8.61635 9.047 9.17775 8.73425 8.5646 9.2159333 9.3943333 9.4491 7.8134 8.2303333 7.5149667 7.8333 8.0729 8.08435 8.4459 8.7877 8.0331 9.1249 9.05025 8.9521 8.52465 8.2431 8.22115 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8.2828 7.8765 8.3418 8.7312 8.5654 8.3825 8.553 8.4412 8.7598 8.7349 8.7497 8.96645 8.5278 8.4391 8.7877 8.8154 8.63705 8.5537 8.51105 9.02975 8.5877 8.4934 8.77575 8.2661 7.7767 8.6229 7.7127 8.4829 9.0367 8.5807 8.64215 9.2027 9.46795 9.1433 8.6969333 9.3241 9.2146 8.6005667 8.8202333 8.8715667 8.8484667 8.2285 8.4536667 8.7393333 8.8620333 8.3671 8.5627 8.5267 8.4833 8.7159 8.6221 8.4222 8.5111 8.4952 8.8412 8.256 8.3437 8.3886 8.5244 8.726 8.6404 8.5699 8.6752 8.6967 8.5593 8.3617 8.6029 8.6966 8.9986 9.2405 8.594 8.6785 8.599 8.5758 8.5879 8.4589 8.4451 8.746 8.3939 8.633 8.51215 8.62995 8.825 8.6619 10.579 8.8782 10.934 12.029 8.7781 8.8795 8.8228 9.476 9.941 +"G67" 7.73445 7.4809333 7.74065 8.45895 7.9675 7.1839 7.5931667 7.48655 7.3504 7.4566 7.73325 8.0654 7.89015 7.9797 7.5549 7.66245 8.2362 8.0854 7.8295 7.618 7.7496 7.3513 8.3805 7.6141 8.1643 7.9089333 7.88012 8.0749 7.61042 7.8663 8.5796 7.4572 7.9568 7.8381333 7.7945 6.3970333 7.94255 7.145 7.77452 8.219 8.08465 8.0095 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7.9164 8.1622 7.6901 8.1787 8.0303 8.2079 7.588 7.60435 7.93595 7.7275 7.89935 7.9175 7.9484 7.68815 7.9544 7.7968 7.9221 7.813 7.9103 7.8143 7.7725 7.6398 8.3909 7.4646 8.229 7.5372 7.9832 7.6602 7.8147 8.0133 7.6765 7.7989 7.7087 7.5908 7.6485 7.4819 7.5525 7.7064 7.4702 7.6286 7.5146 7.3573 7.5778 7.5662 7.4672 7.4308 7.4996 7.6176 7.5638 8.1797 7.7852 7.8566 7.7914 7.5634 7.9717 7.8927 7.5689 7.3711 7.3066 7.2112 7.2677 7.5088 7.3012 7.46396 7.37858 7.44246 7.32486 7.6061 7.6422 7.2588 7.2776 7.5759 7.2467 7.3415 7.5918 8.0995 7.5416 7.4942 7.4237 7.3964 7.4107 7.4393 7.511 7.587 7.6361 7.4533 7.9902 7.8643 7.8364 7.6106 7.4281 7.369 7.3847 7.5433 7.6115 7.5235 7.7426 8.0389 7.5567 7.9624 7.8411 7.9919 8.1291 7.7472 7.8602 7.7546 7.8335 7.5414 7.5347 7.5617 7.5631 7.2198 7.2526 7.4067 7.5413 7.4287333 7.3175333 7.4979333 7.3760333 7.6405 7.8976 7.5268 7.6572 7.5338 7.3015 7.5397 7.9797 8.219 7.8295 8.2144 8.2602 7.4539 8.4846667 8.5377333 8.3154 8.4324333 8.2666 8.0649667 8.4603333 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10.287 9.4254 9.7906 9.8438 9.8623 9.5006 9.7447 9.6859 +"G87" 10.11 10.1076667 9.89875 7.4471 9.75915 8.9045 10.15 10.016 10.419 9.78185 9.7197 9.1977 9.17935 7.2259 8.8105 10.177 8.102 9.61025 8.0678 10.0138 9.126 10.2012 9.56455 6.9504 9.6542 9.9891667 10.2914 10.207 9.74108 10.251 8.6076 7.3384 10.656 10.0965333 9.3308 9.4834333 8.0307 8.5655 9.8215 7.1298 8.1532 9.1675 7.4426 9.7011 7.1978667 8.2475 10.0924333 8.9302 9.6094 10.6723333 7.5672 10.141 10.29375 7.9432333 7.6692 10.0553667 8.9446 8.9292 10.942 8.2166 8.1391 8.2475 8.2086 9.03625 8.8206667 9.8270333 7.5672 9.0921 7.1293 9.6221 9.4376 9.4086 9.873 9.78472 10.3588 9.6754 9.57786 9.96882 8.52285 9.5275 7.225 10.087 9.8942667 10.1553333 10.0069 10.2633333 9.9632667 9.9847 8.3067 9.7089333 7.4096 6.6801 8.4918 9.9482667 9.8076 9.0583 9.6948 7.6971 8.2892 8.6245 7.7839 8.6782 9.2491 9.5102 9.3795 7.3547 9.4347 8.9392 9.3862 9.0486 10.621 9.3524 8.7052 9.3201 9.1139 10.459 9.4704 10.289 9.5283 9.0738 8.2203333 8.1228 9.884 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10.31 10.629 10.663 10.873 10.508 10.703 10.556 10.823 10.601 10.05 10.783 10.158 9.9472 9.4215 8.7996 10.298 10.116 9.5531 8.9713 10.519 10.553 10.544 10.218 10.639 10.2063333 10.309 10.942 11.1713333 10.8596667 10.439 9.9893 10.272 10.731 10.386 9.9769 10.412 9.6967 8.6919 7.2259 7.1298 7.1632 8.2446 7.2069 7.531 7.2627333 7.5643333 8.1253 8.1550667 8.3383 7.4133667 7.4569667 7.5185333 7.3064667 6.8934667 7.611 7.7071 7.3086 9.3107 9.6966 9.9964 9.9562 9.841 9.7807 9.9456 9.8332 9.4203 10.627 10.263 9.3379 9.6917 8.7905 9.1147 9.0061 7.8974 7.9187 8.6129 10.219 10.207 9.9522 9.8101 9.414 9.5733 9.6036 10.8806667 10.6265 10.6733333 10.62975 10.8025 10.7435 10.492 10.642 10.653 10.676 10.5485 10.567 10.9466667 10.871 10.612 10.71375 7.2627333 7.5643333 8.1253 7.4569667 7.5185333 8.1844 8.3383 7.4133667 7.3064667 6.8934667 9.7355 9.7441 9.9856 9.0023 9.3053 8.915 8.9594 7.3225 10.445 10.665 9.9398 10.402 10.433 10.376 10.307 9.214 10.272 10.233 10.036 10.006 10.581 8.9707 9.9641 8.8874 8.6945 10.045 9.9774 9.8406 9.9471 10.049 10.175 10.096 10.544 10.867 11.186 10.982 10.7585 10.973 10.0955 10.7175 10.7515 10.828 10.2365 10.941 10.6155 10.669 10.1715 10.861 8.588 7.6692 7.5672 8.3049 7.9637 6.6952 9.3771333 7.4097 8.2371 7.2945 7.47585 9.6846 7.24275 7.0394 9.7908333 9.3850667 8.5933 7.9273333 10.1058 9.1394 8.3802333 8.0574333 10.727 10.082 9.5175 8.8201 9.0306 10.949 10.53 9.7774 8.5801 9.1699 10.131 10.206 9.851 8.9238 8.7545 11.102 10.594 10.801 10.356 10.651 10.544 10.52 10.442 9.4599 8.9372 10.654 10.602 10.415 10.326 10.124 10.773 10.219 10.52 9.8758 10.8195 10.6075 10.8045 9.88265 10.266 9.7066 9.1901 10.693 10.597 10.273 11.447 10.552 10.501 9.8973 +"G88" 7.4892 7.4363333 7.9664 7.98095 7.447 6.9435 7.1604667 6.97785 7.2615667 6.8937 7.57915 7.6178 6.9824 7.5166 7.0921 7.1231 7.4709667 7.28265 6.8686 8.1868 7.323 6.5611333 7.0423 7.0537 7.3659 6.8833 7.78454 7.2607 7.69448 7.66795 7.1601 8.2971 7.3216 7.4989667 7.045 7.7500333 7.6752 7.09585 7.26512 7.2746 7.51385 6.1192 7.7828667 7.0074 7.1834667 7.0243 6.9806 8.2971 8.0169 7.5261 7.0784 7.0012 6.556275 7.9181333 6.7124 7.2839 7.75625 7.2488 7.606 7.8589 7.4274333 7.5995 7.7808 7.61005 7.3339667 7.2279333 8.2637 8.0007 8.2637 7.1349 7.05715 7.07955 7.1046333 7.04066 7.23838 7.2695 7.40916 7.26822 7.46795 7.1421 7.1847 7.5928 6.6115667 7.5934333 7.5176 7.3636 7.3718333 7.4062 7.5134 7.2703 7.7228 6.8146 7.06395 7.1546333 7.6404 7.3001 7.0745 7.4143 7.809 7.3664 8.0607 7.6047 6.8102 9.2075 7.5639 6.9597667 7.3613 6.6704 8.0655 7.1169 7.6581 8.8129 7.3931 7.948 7.7736 7.2644 7.1361 7.8199 7.5645 7.5046 6.8874 7.6332333 6.8355 7.3293667 6.8146 7.075925 7.7952 7.1066 7.3453 7.7736 7.2467 7.4278 7.2331 8.199 6.89025 7.3211667 6.8841333 7.7417333 7.695 7.3811667 7.0706667 7.2739333 7.4167 7.1089667 6.6626 7.7217 7.4143 7.30045 7.5748 7.1846 8.8194 7.2978 7.2001 6.9252 8.651 9.274 7.1988333 7.2494 7.2648 7.14965 7.49865 6.90655 8.04195 7.0641 7.2981 7.0366 7.038 7.8162333 7.3345 7.2533333 7.3771 7.2567 7.1322 7.7703 7.15225 7.4525 7.1776 6.6704 7.1518 7.03085 7.8593 7.3699 7.188 7.5682 7.27745 7.47265 7.5874 7.4216 7.26055 7.05335 7.6178 7.0901 6.9837 6.9666 7.3334 7.7808 7.5975 7.0254 7.5978 6.8663 7.2557 7.1041 7.6472 6.9613 7.1606 7.6581 7.9417 7.6119 7.6806 7.1123 7.7808 7.2298 10.187 6.8638 7.0884 7.0504 7.8867 7.0244 7.5589 6.7323 7.1179 8.0969 7.5533 6.4909 7.1601 6.9317 7.5862 6.9312 6.8455 7.7859 8.5089 7.1518 7.7228 7.1499 7.3659 7.66706 7.57016 7.27308 7.17246 7.2212 6.9837 7.1914 6.9374 7.8589 7.1714 7.0522 7.2246 9.7642 7.1351 8.1015 6.9965 6.99 7.4 7.9035 6.3141 7.0254 7.3549 8.196 7.9947 7.0765 7.2402 8.5019 7.4496 7.15 7.6676 7.2211 7.2811 7.1032 7.5335 6.7071 7.9487 7.2144 7.0895 7.1823 7.5079 8.6645 7.1155 7.1424 7.6199 7.8642 7.4797 7.1478 7.1086 7.5334 7.2571667 7.4845667 7.1481 7.4175333 7.5254667 7.5655667 7.2911 7.9559 6.3151 6.125 7.9544 7.2162 7.2883 8.1611 7.2554 7.8199 8.1557 7.8113 7.8527 6.8102 7.2648 7.8471 7.0998333 7.4078667 7.8032667 7.584 7.6435 7.4611667 7.8838667 7.1797 7.2402 7.045 7.6404 6.6626 7.3789 7.2481 6.6577 7.4811 8.0007 7.0745 7.5761 7.1669 7.0726 7.2686 7.2599 7.0935 6.9613 7.5439 7.5935 6.6707 7.5079 7.0254 6.5695 7.0854 7.339 7.1823 6.8208 7.2861 7.3597 7.2789667 7.34985 7.2499333 7.50305 7.452 7.5417 7.3224 7.7866333 7.84905 8.12225 6.9894 7.0117 7.4744 7.6782667 7.7795333 7.101125 6.9313 7.8877667 7.5275333 7.6143333 7.3673667 7.4367 7.1691667 7.4293333 7.5183667 7.7279 7.3453 7.1556 7.2557 6.8097 7.8527 7.0714 7.5125 8.6718 7.045 7.0608 7.4447 6.8304 7.9243 7.2803 6.6704 7.8898 7.1478 7.1526 7.2298 7.2739 7.4314 7.4539 7.0854 7.4694 7.2599 6.5502 7.1351 6.8793 7.3413 7.1467 7.7736 7.4496 7.809 6.96 7.2144 6.9435 7.41075 8.75425 6.8875 7.1213 7.20995 6.1874 7.517 7.534 7.4149 6.9495 7.1112 7.0112 7.4349 7.1821 7.6144 7.2653 6.9077 7.1823 7.4391667 7.2961 7.4644 6.7202 7.6574 7.2712 7.87065 8.1015 7.2099667 7.0446667 7.3139 7.2652 7.0802333 7.4452667 7.3476 7.1374 7.544 7.5335 6.1192 6.9452 7.6833 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7.9013333 7.7951 8.7180333 9.7278 8.1941333 7.3822 7.8059 7.8319 7.7261 8.6544 9.2283 8.6942 8.3985 8.1236 7.8327 7.9889 8.2271 9.1686333 8.6874 8.8404 7.79215 7.6205 7.7128 7.7673 8.04055 7.8009 7.70875 8.3071 8.3146333 8.6554 8.3136 7.8743667 8.0548667 8.5387 9.1284 8.6469 8.1367 8.6579 8.4779 7.9981 7.57795 8.4979 8.8739 8.9517 9.6027 9.89095 7.5735 7.94025 8.6492 8.7212 8.69555 8.7394 9.0401 9.6066 9.1251 8.3074 7.8838 8.5157 7.9254 8.4049 8.8817 9.4917 9.1262 7.4068 7.1047 7.4368 7.1458 7.3691 7.4615 7.3506 7.5537 7.4528 7.4324 7.6462 7.683 7.3441 7.6412 7.6625 7.8642 8.2331 7.831 7.5137 7.7749 8.072 8.0454 8.6435 9.0337 9.6001 9.5971 7.9662 7.8399 8.0525 8.6188 7.7495 8.8449 8.2669 7.50518 7.51416 7.47448 7.4765 7.854 7.8044 7.7274 7.6223 7.6895 7.969 8.0427 8.2167 8.2628 7.8394 7.8091 7.7231 7.7783 7.9835 8.0803 8.1269 8.2699 9.3601 7.6636 7.4153 7.4188 7.6747 7.2497 7.436 7.475 7.302 7.4054 7.4017 7.4792 7.2862 7.5494 7.4905 7.3925 7.6685 7.6494 8.2309 7.3759 7.5477 7.5278 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7.2276 7.51035 7.29145 7.54315 7.34055 7.3021 8.2529 8.8352 8.666 9.5655 9.2253 9.0869 7.8802 7.90675 8.43645 10.27405 9.73255 7.8638667 9.36175 10.124 7.725 7.6177667 7.8718 8.7791667 7.9001667 7.8871333 8.1109 8.5707333 7.6177 8.0356 8.3415 9.3706 8.8596 7.7831 8.0079 8.0383 8.0699 8.4565 7.6714 7.5154 7.6019 7.9913 8.2694 7.4338 7.5137 7.7501 7.9156 7.8971 7.6275 7.7192 7.7023 7.9652 7.9344 7.2773 7.3974 7.6521 7.6112 7.6902 7.8282 7.7192 7.8726 7.8672 7.68355 7.64595 7.7992 8.0625 7.686 8.7313 9.1285 7.8967 8.4845 7.7706 7.5891 9.8329 8.215 9.2961 +"G91" 8.10705 8.4953 7.98515 8.2688 7.63345 8.5427 8.1739333 7.62265 8.2511667 7.3261 8.0709 7.8844 7.83605 7.8324 8.4913 7.5387 8.0019333 8.00525 7.0182 7.17685 8.6353 8.0315667 8.00465 7.3513 7.9064 7.7863333 7.63684 8.0097 8.00502 7.45825 7.866 7.3987 8.0229 8.1310667 7.6795 8.027 8.0366 9.1641 7.91526 7.7828 8.09685 8.6825 8.4334333 7.4381 7.9756333 8.6293 7.6900333 8.6301 8.4185 7.7554667 7.4605 8.7737 8.06145 8.1060333 7.7927 7.9008667 8.7892 7.7956 8.6763333 8.8206 8.0374667 8.6293 7.6277 7.12645 8.2927667 7.5904333 7.4605 7.6463 7.5442 7.7492 7.76255 7.70905 7.6898 7.66504 7.58028 7.8268 8.30856 8.00878 8.361 7.536 7.5331 8.2948 8.0832667 7.8469 8.1192 7.9617333 8.0861667 8.0027 7.5368 7.997 7.7005 8.0773 9.2793 7.7731333 7.4645 7.7487333 7.9816 7.7131 8.1068 8.0659 7.6671 8.0172 8.788 8.814 8.676 8.2899 9.0981 8.1731 8.3355 7.931 7.9638 8.3299 8.0829 7.9321 7.5618 7.8992 7.7033 8.3937 8.0619 7.9274 7.7404333 7.6434667 7.6239333 7.9823667 7.9686 7.946925 7.7582 7.8621 7.6778 8.2562 7.6567 7.8769 7.5862 8.7539 8.0567 7.9315333 7.9739667 8.2788667 8.5310667 8.0503667 7.8150333 7.8335 7.5977 8.2473333 8.2299 8.6062 9.0709 8.17865 7.9673 7.4299 9.0225 8.5403 8.2928 8.6219 9.0293 8.6209 8.3430333 7.3255 7.456 7.3022 7.0692 8.02815 8.5163 8.24425 8.3871 8.4475 7.9411333 8.1576 8.0246333 8.0780667 7.4094333 7.5298333 7.7672 7.6045 7.11315 8.0924 8.0235 7.5871 7.834 8.18895 8.2032 8.3031 8.13255 8.18935 8.05675 8.4461 8.0644 8.99875 9.0755 9.1679 7.9652 7.9192 8.3537 7.8272 7.6966 7.5453 7.7511 7.9914 7.7558 8.1488 8.3885 7.8793 7.7176 7.8324 8.0582 7.7984 7.6854 7.8449 7.92 8.029 7.7066 7.8357 7.9898 7.7418 7.621 7.5764 7.7996 7.815 7.1555 7.816 7.3805 8.5256 7.7724 7.6224 8.1499 8.5247 9.1502 9.3656 8.9435 8.8414 8.3917 7.9382 8.8277 8.0558 8.4289 7.96656 8.48646 8.49236 7.90646 7.5328 7.6434 7.4768 7.3524 8.0301 8.03 8.6434 8.3627 8.9443 7.3015 7.5421 8.0068 8.4024 8.1485 8.5671 8.2238 8.3087 8.2676 7.6247 7.7651 7.9334 8.1272 7.8298 8.2353 7.8358 8.0907 8.2553 7.9078 7.5511 8.1251 7.9094 7.8311 8.2904 8.3205 8.1093 7.887 8.0447 8.2196 8.3041 8.0204 7.8468 7.7617 7.7372 7.9732 8.2781333 8.3362 8.7947333 7.2189333 7.1729333 7.1333 7.1620333 7.2031 7.6705 7.0688 8.3273 8.6865 8.2025 8.3744 8.4893 7.8324 7.7828 7.1057 8.432 8.1732 7.225 8.1475667 8.1070333 7.7763667 8.5318667 8.2816667 8.5747333 7.9312667 8.0140333 8.4437333 8.1153333 7.2317 7.7788 7.9118 7.28 7.3036 7.3426 7.3843 7.1653 6.8752 6.9643 7.387 8.8951 7.9806 8.4972 9.5097 7.9741 8.2843 7.6564 7.8125 7.421 7.3619 7.638 8.7543 8.4584 8.2186 8.4721 8.28 8.2077 8.2275 7.9851333 7.9775 8.082 7.897025 8.02375 7.88195 8.5722 7.9092333 7.98175 7.94555 8.54025 7.9352333 7.9194667 7.8394333 8.4258 8.027625 8.1475667 8.1070333 7.7763667 7.9312667 8.0140333 8.42984 8.2816667 8.5747333 8.4437333 8.1153333 7.7278 7.0261 7.2812 7.8808 7.4256 7.4638 7.936 7.7688 7.7284 7.5016 7.8302 7.7273 7.9962 7.5949 7.6964 7.7151 7.5896 8.4011 8.553 7.8424 8.7959 8.4374 7.0772 7.4167 7.2844 8.4063 8.6322 8.7325 8.2561 8.47 8.2342 8.0569 7.9788 7.1486 7.0451 7.4907 7.8452 7.65825 8.53575 7.69315 7.8418 7.6852 8.42065 8.12715 7.89045 7.69435 8.37255 7.9846 7.8148 7.7927 7.4605 9.0384 7.9774 7.5304 7.8707333 8.55215 8.27225 8.19365 8.25705 7.8140333 7.9405 7.8913 7.5143667 7.8507667 7.9235333 8.207 7.6049333 8.4731 8.2941333 7.4404333 8.4752 8.8378 8.986 8.8347 8.0612 7.9471 8.8301 9.0787 8.8736 8.1347 7.8048 8.132 8.5979 8.3467 8.4151 7.8605 8.2032 8.0523 8.3956 8.0833 7.9981 8.046 8.4908 8.2998 7.9472 7.8855 7.6317 8.2272 7.8738 7.9763 7.7239 7.5824 7.9332 8.3859 7.70115 7.5923 7.7879 8.8904 7.7379 8.4587 8.2342 8.7189 8.7449 7.662 7.5852 7.6544 7.2209 7.9635 +"G92" 10.2475 11.022 10.841 10.23 10.4485 10.219 11.3373333 10.8335 11.0283333 10.7795 10.7905 8.9601 10.1415 8.9485 10.102 10.5935 10.882 10.447 10.394 10.748 10.103 10.241 10.667 10.714 10.618 10.289 10.4856 10.981 9.69462 10.8895 10.618 9.7975 10.614 10.919 11.081 8.3645333 10.7055 8.76785 10.4334 9.0651 11.056 9.8613 8.6909 10.053 11.3763333 10.572 9.9781333 10.091 11.205 10.1193667 10.573 8.7336 10.1745 9.3198 10.801 10.6263333 10.2095 9.867 10.2338667 10.193 9.1227667 10.572 10.914 7.0458 10.1713333 9.2117 10.573 10.732 9.8088 10.612 10.1005 10.24 10.385 10.7588 10.8936 10.371 9.54006 9.70532 8.81535 10.621 9.7795 10.226 10.5986667 10.747 8.5646333 8.8293 10.8203333 10.7026667 10.548 10.4163333 10.891 9.7826 8.96525 10.5846667 10.724 10.0709333 10.618 10.409 10.532 10.719 11.321 10.7655 9.5718 9.9679 10.075 8.4792667 9.928 9.1753 10.194 9.6626 10.623 10.068 9.5708 9.4442 9.6774 10.347 10.501 10.148 10.225 10.439 10.1325333 9.6546667 9.1975667 10.2577 10.271 8.815325 10.485 10.769 10.812 10.289 10.426 10.736 9.8359 9.0832 10.8655 10.249 10.063 8.8216333 8.94 10.1633333 10.3246667 9.9786667 10.781 10.0756333 10.574 10.024 10.661 10.2365 10.552 10.993 10.779 10.719 10.867 10.073 10.161 10.149 9.5762 9.4698 10.302 7.1173 7.24735 7.7773 9.20065 8.4667 8.97565 8.9669 9.8915 10.0136 10.229 9.3675 9.5391333 9.3023 9.7508 10.684 9.5377 10.915 10.772 10.569 9.2735 10.4555 10.149 10.2475 10.2635 10.452 10.5785 10.206 9.9026 9.85985 9.8854 10.071 10.435 10.522 10.027 10.24 10.476 8.808 9.7062 9.199 9.9823 10.497 10.146 9.9854 10.418 10.72 10.396 10.643 10.41 10.343 10.053 9.3965 10.544 10.513 10.698 10.626 10.477 10.575 10.953 10.925 10.897 10.479 10.997 10.694 10.437 10.641 10.849 10.382 10.861 11.031 9.0968 8.8032 8.5715 8.8512 8.8659 8.9458 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6.0399 5.9386 6.6186 5.7038 6.3211 5.8078 6.66325 5.70605 5.743 5.9423 6.44805 6.04625 6.0347 5.8126 6.7364 5.86455 6.8006 6.2674 5.9924 6.2664 6.5871 6.1614 6.0182333 5.78735 6.4029 6.5111 5.9965 6.2262 6.19115 5.7959 5.7742 5.9683667 6.0803 6.3875 5.8188333 6.2022333 6.4149333 5.9489 6.3046 6.2736 7.1002 6.3217 6.3676 5.5049 6.3103 6.695 6.3035 6.1605 6.0032 6.0225 6.1389 6.18 5.8813 6.1933 5.8072 5.9973 5.9897 6.1151 5.8841 5.8561 5.6915 6.1213 6.1066 5.7104 5.7772 6.1325 5.7772 5.8767 6.0181 5.9567 6.1012 6.2099 6.1767 5.82065 5.7853 5.9954 6.0904 5.6883 5.7377 5.6371 5.7228 5.8808 5.4247 6.4287 5.6033 5.6969 +"G115" 8.8947 8.9926333 8.99245 9.40415 9.02755 11.172 8.9393333 9.1514 9.1489333 9.05565 8.7108 9.8562 9.01245 9.3737 10.23 8.70795 9.9783333 8.88925 9.4536 9.58045 9.85395 10.4236667 9.0366 10.333 9.3126 9.2699667 9.16684 9.5402 9.38188 9.44985 9.3591 10.174 9.4826 9.4045667 9.603 8.8721667 9.2267 10.1385 8.91316 8.9653 9.2423 10.131 9.4486333 9.7154 10.358 8.7898 9.0140333 9.6782 9.9296 9.5754667 9.2166 9.51695 9.27695 9.1881333 10.01 9.2673 9.72455 10.194 10.796 10.071 9.6973 8.7898 10.017 9.37885 9.7345333 9.7718667 9.2166 9.2027 10.193 9.3151 8.6749 8.504 9.2523333 9.09362 9.02614 9.3786 9.63046 9.12488 9.36845 9.5806 9.5904 9.7769 9.4284667 9.3279667 9.4780333 9.6302667 9.6323667 9.3199667 9.5798 8.8213 9.6974 9.9013 10.228 9.1954 9.1663 8.6707 8.8841 9.4106 8.5586 9.0565 9.2341 9.1 10.082 9.7699 9.9367 9.3741 9.7261 9.8897 9.8042 9.812 9.538 9.8017 9.9517 9.5373 9.7143 9.3305 9.4571 9.4799 10.077 9.7517 10.2383333 9.3127333 8.8497 9.4278667 9.2757 9.01055 9.9375 9.8208 10.262 9.2294 9.3607 9.3002 9.6023 9.5892 8.88665 9.3926 9.162 9.2411333 9.0508667 9.3001 9.2569333 9.5078667 9.7392 9.5407 9.0047 9.7403 9.9012 9.4385 10.316 10.932 9.5919 9.5933 9.8564 9.8496 10.154 9.6485 10.1396667 8.8858 9.366 9.3268 9.75185 10.452 9.4845 9.8388 9.7192 9.749 9.9643 10.0819667 9.9945333 10.0210667 9.9218667 9.9658333 9.3935 9.6261 9.01785 9.5495 9.3892 9.3392 9.5778 9.4032 9.2086 8.89385 8.83805 8.82595 8.9308 9.7806 9.65985 10.3105 10.143 9.89915 9.8481 9.6955 8.7013 9.4117 8.7707 9.3451 9.41 10.004 9.4138 9.5699 8.6738 9.2796 9.4819 9.1219 9.579 9.174 9.3032 9.3808 9.1867 9.0539 9.5014 9.4559 9.3326 9.332 9.3121 9.384 9.3281 9.3618 9.4381 9.3432 9.3366 9.73 10.026 10.383 10.459 10.278 10.6 10.529 9.7784 9.654 9.3904 9.4647 9.5464 9.2263 9.4331 9.4362 9.41918 9.41184 9.3296 9.3845 9.403 9.235 9.0861 9.4675 9.9337 10.456 10.349 9.9509 9.5231 9.4974 9.538 9.7336 9.9276 10.592 10.531 10.716 10.167 9.2647 9.2329 9.2536 9.6771 9.2184 9.4574 9.1548 8.8087 9.0309 8.7742 8.7449 9.2889 9.2999 9.2855 9.3895 9.4482 9.588 9.1647 9.396 9.4358 9.6369 9.4323 9.2581 9.2494 9.1553 9.115 9.2457667 9.1849 9.9772 8.5683 8.8727 9.0608333 9.0462333 9.1412333 9.2421 10.108 10.367 10.36 10.562 10.679 10.623 9.3737 8.9653 8.7961 8.8482 9.1405 9.2572 8.9476 8.9879667 9.2344667 9.3429667 9.6731 8.8246667 9.0196 9.2968333 8.8818667 8.7547 9.6371 9.1176 8.8986 9.2807 9.1155 9.3966 9.5939 9.5459 10.019 10.284 10.33 9.8696 8.9578 9.5255 9.6631 9.283 10.127 9.4511 9.2721 8.5847 8.8651 8.7218 9.4543 9.41 9.15 9.9257 10.53 10.064 10.725 9.4759 9.3156 9.4099667 9.279525 9.33435 9.41995 9.3611 9.3049667 9.2651 9.3182 9.5321 9.2917333 9.4194333 9.287 9.3894667 9.255825 8.9476 8.9879667 9.2344667 9.0196 9.2968333 9.3951 9.6731 8.8246667 8.8818667 8.7547 9.0453 9.2309 10.109 9.0318 9.4596 10.301 9.3052 8.545 9.5094 9.4932 8.9613 9.297 9.3648 9.3023 9.6512 9.0595 9.3408 9.4874 9.5076 9.5874 10.262 10.608 8.9785 9.0641 9.493 10.311 10.569 10.677 9.5433 9.5998 9.5108 9.9467 9.4373 9.6076 9.5742 9.8444 9.4749 9.6619 9.52995 9.54085 9.64605 9.424 9.55175 9.30295 9.1357 9.4283 9.5814 9.60625 9.6798 10.01 9.2166 11.043 9.817 9.7261 8.7992667 9.1215 8.4115 9.06675 9.31445 8.8648 8.7375 8.853 9.5373333 9.4373 9.3231667 9.6207667 9.3392667 9.4450333 9.2933333 9.3608667 9.9312 10.002 10.208 10.032 9.8024 9.7663 9.94 9.9197 9.8846 9.6106 9.095 9.0667 9.5363 9.5422 9.1482 9.7616 9.8599 9.6508 10.175 9.7516 8.965 8.8616 9.1203 8.6944 8.6123 9.2489 9.1349 9.256 9.0085 8.9188 9.498 9.3381 9.4893 9.4867 9.47815 9.34885 8.96635 9.27105 9.2807 10.03 9.81 10.028 10.583 9.2672 9.9188 10.294 9.7086 9.9203 +"G116" 9.099 10.1756667 10.13235 11.616 11.0365 10.227 10.9283333 12.0755 9.7609667 11.4325 10.285 12.009 11.249 8.4893 9.7368 10.3675 9.9203333 10.9005 11.635 10.456 9.2633 9.7061333 11.2675 10.717 10.282 11.3096667 11.0342 10.224 9.97168 11.036 11.333 10.548 11.526 11.071 10.362 9.9112 11.841 10.5295 10.3528 9.9431 11.1705 9.1742 8.7507 9.5767 10.8456667 11.57 9.1559333 9.2081 10.588 11.6363333 12.215 9.925 9.23765 8.5729333 10.264 10.1129333 9.52765 9.7408 11.3716667 10.077 9.1818667 11.57 11.676 8.9482 10.861 9.1824333 12.215 9.907 9.44 11.483 9.5266 9.8723 11.2713333 11.1188 11.0418 10.653 9.35218 9.279 8.6038 11.763 11.092 10.815 10.0290667 11.054 11.3 11.656 11.7896667 10.9336667 10.808 9.8923333 11.774 11.963 10.5495 10.1906667 10.373 9.5638667 10.088 12.154 11.05 11.772 12.277 10.582 9.1759 9.3832 9.4091 8.4094 10.562 9.0579 10.527 9.2676 11.219 10.471 9.258 9.3714 9.4986 10.699 10.77 11.25 10.041 10.75 11.1963333 10.4803333 10.4255667 10.7819 11.97 9.665625 9.2422 9.6255 10.489 10.978 11.607 11.574 11.455 9.8231 9.4967 9.3025667 9.2186333 9.7262667 9.1865333 9.2039667 9.4154667 8.7368333 11.845 10.0883 11.445 9.3662 10.307 10.20905 10.386 12.107 11.345 11.651 11.813 9.2745 9.7185 9.6268 9.8372 10.739 10.257 8.99065 9.16125 10.11 9.597 9.4175 9.92845 10.3015 10.1613333 9.7797333 9.9948 9.7976667 8.9633333 9.5653667 11.678 11.836 12.246 9.2747 9.9311 10.905 9.4308 9.53915 9.36325 9.4001 9.6034 11.457 11.305 9.64495 9.2031 8.6973 9.0444 9.4618 10.35 10.445 10.851 12.031 11.276 9.0406 11.113 9.6717 8.7619 10.007 11.2 11.503 9.74 9.605 9.4224 9.0524 9.4366 9.7074 9.3901 9.7302 9.7455 12.081 11.818 11.722 9.5007 12.193 11.997 11.901 11.172 10.107 10.67 10.131 10.526 12.5 12.304 11.831 12.231 12.126 9.7068 9.4975 9.2512 8.8211 9.361 8.2564 9.1348 11.4004 11.2314 11.3202 11.1904 11.216 11.32 10.828 10.708 10.845 11.528 12.343 12.228 8.5919 10.315 10.587 10.335 10.888 11.634 12.982 12.542 12.293 12.045 10.994 10.54 10.872 10.761 10.843 10.49 10.929 10.666 10.78 10.493 10.938 10.972 10.677 10.936 11.567 10.733 10.098 10.402 11.503 10.748 10.03 10.053 11.449 11.071 11.181 10.771 11.7693333 10.3488667 9.4511667 10.4833333 11.306 11.6116667 11.6093333 11.5636667 9.6357 11.919 12.345 12.426 12.438 12.095 11.463 8.4893 9.9431 11.281 9.6024 11.657 10.13 9.9960333 10.3366667 11.867 10.767 10.1673333 9.4462333 11.366 11.677 9.3388667 10.0000667 11.527 11.45 11.329 12.377 11.088 10.644 9.3692 12.227 12.061 11.988 11.612 9.5741 10.998 10.099 9.4104 9.7465 8.8028 9.6978 9.75 8.9772 9.4869 11.562 10.875 10.58 10.036 10.901 12.007 11.238 12.006 11.503 11.545 11.5026667 10.78675 11.413 11.2995 11.72 10.8063333 11.315 11.397 11.7065 10.9943333 11.3626667 11.431 11.4813333 10.9105 9.9960333 10.3366667 11.867 11.366 11.677 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10.427 10.377 10.305 9.7402 10.502 9.8506 10.265 10.18 +"G431" 12.3315 6.835 8.67865 8.80895 12.709 7.9269 7.9611667 10.5325 7.0002 11.223 8.70005 8.6665 12.9135 8.5623 8.6559 12.519 7.6379667 12.8135 9.065 12.261 8.44665 6.7927 12.946 8.6857 8.6609 12.744 12.556 9.3873 8.2971 8.25455 8.3854 8.772 9.3759 8.4512 8.729 12.194 8.67695 11.325 12.1798 8.1059 8.38645 9.1313 8.4350667 8.6464 8.5571 8.455 12.6686667 8.4756 8.1045 12.577 7.9669 12.9245 8.149075 7.8720667 7.9628 9.2315 10.5405 8.1895 8.7798667 11.325 8.3405667 8.455 8.777 7.96145 13.1576667 8.1225 7.9669 8.6331 8.234 8.7732 11.845 11.4155 12.78 12.4128 11.381 9.2214 8.86372 7.76032 8.2899 8.2002 8.0947 9.3866 8.5061667 8.2499667 8.3263 8.2105333 8.3481 8.4531333 8.6846 12.627 8.0225 8.5932 11.462 8.2854 12.271 9.3677667 12.272 8.8213 8.7758 8.2115 8.5497 8.36565 8.7857 8.5649 8.8571 8.9093667 12.842 8.9007 9.2234 9.2051 9.1146 9.5188 9.1514 11.253 11.49 9.1316 9.686 9.8435 10.417 9.2848 12.434 8.4343 12.267 8.6625333 8.9105 10.95925 8.4408 8.3998 8.1371 8.4309 12.089 12.389 8.2372 12.665 8.87575 8.3435667 8.0522667 8.2187667 8.2643333 8.1329 8.3682333 13.0113333 8.324 10.3589 8.9968 9.4278 9.8111 8.6775 8.2322 8.4957 8.0831 11.958 11.534 10.312 10.428 9.935 10.2097333 8.9122 8.7805 7.71365 7.4922 7.7752 8.7197 8.4507 8.3848 8.46705 13.2933333 13.432 13.509 8.4439667 8.3029333 8.3567333 9.2005 8.481 8.201 8.5873 8.3858 10.744 8.192 13.278 8.8691 8.3139 8.4307 8.7223 8.7986 13.4355 12.74 8.98605 8.8282 8.6535 8.2883 8.398 8.7442 8.8156 10.422 8.0957 8.958 8.3341 8.2486 8.6511 8.9771 9.0814 12.715 12.58 12.882 12.409 12.628 12.461 11.951 10.4 12.811 12.908 12.608 12.309 12.652 12.836 12.9 12.873 11.549 12.61 12.379 12.398 12.172 10.341 9.1911 9.4329 8.131 7.6442 8.7942 8.6727 8.5353 8.2914 8.3679 8.4005 8.3996 7.98618 7.97194 8.0428 7.73912 8.5252 8.6785 8.5998 8.8668 9.4277 8.2025 8.268 7.9435 11.166 8.7333 8.8712 8.735 9.1497 8.226 8.0182 8.2245 8.1653 8.594 12.193 12.413 12.834 13.206 12.01 11.118 11.717 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12.977 13.21 13.075 13.018 13.013 13.203 13.065 13.137 12.8835 10.6605 12.8025 10.482 12.836 10.9885 12.934 9.527 12.879 10.6845 12.776 10.8445 7.4997 7.9628 7.9669 7.8369 8.3182 8.1979 11.5233333 10.9315 8.9386 9.03365 8.68045 12.0866667 8.85675 8.869 11.377 12.341 11.2103333 9.5131333 10.0760333 11.0553333 10.4719667 9.1126667 10.063 8.4553 8.7781 8.14 8.1702 9.4912 8.0931 7.8908 8.1143 8.5735 12.817 12.729 12.866 12.772 8.9408 8.1878 7.6323 8.1499 8.4048 8.5314 12.648 12.497 12.291 10.245 9.1498 12.381 12.653 12.774 12.474 12.573 12.049 12.371 12.429 12.024 11.98 12.4655 11.123 9.09645 12.658 11.493 8.0544 11.25 8.489 12.594 11.563 7.671 10.525 8.1775 +"G432" 8.5348 8.4567333 8.29985 8.05115 8.28285 8.1792 8.3543333 8.62555 8.8577667 8.56365 8.4628 8.4414 7.8814 8.0273 7.9015 8.3116 7.9640667 8.03355 8.0062 8.57735 8.5449 9.089 8.09835 8.4497 8.4166 7.9667333 8.16172 8.2887 8.20476 8.08145 8.4724 8.5403 8.3067 8.5626333 9.4794 7.7984667 7.89565 7.40425 8.10836 8.5266 7.93545 8.3276 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10.55 10.646 10.482 10.494 10.517 10.222 10.462 10.531 10.579 10.5115 10.431 10.11 10.183 10.061 9.9788 10.793 10.926 10.274 10.435 10.776 10.276 10.357 +"G506" 6.12835 5.7305333 5.92135 6.0223 5.7664 5.6867 5.9170333 6.00475 5.9407333 6.01935 6.0032 5.4971 5.80485 6.257 5.8862 5.75935 5.8218667 6.106 5.8212 6.3947 5.8183 5.6863333 6.0129 6.163 5.8594 5.9372333 6.31896 6.0957 5.8492 6.21215 5.8939 6.3532 6.1016 5.9577333 5.997 6.0134 5.90715 5.84765 5.95106 6.1731 5.7613 5.8759 6.1460667 5.8935 6.2343333 5.8225 5.9344667 5.9123 5.8111 5.9682 6.5529 6.04435 5.970875 5.9277 5.8331 5.8186667 5.94665 5.8747 5.8617 5.6094 6.2044667 5.8225 6.0661 6.0838 5.9120333 5.9981333 6.5529 5.801 6.48 5.9288 5.78325 5.2866 5.8644333 6.56 6.19512 5.9159 5.71526 5.83622 5.8721 5.941 6.4667 6.1059 5.8808333 5.9198 5.7706667 5.9132 5.8315 5.9738333 6.2401 5.9113667 6.0522 6.1941 5.92585 5.7935 5.757 5.864 6.1904 5.9479 5.9747 5.6163 6.2588 5.75135 5.8941 5.8854 6.0944 6.0667667 5.8364 5.9094 5.9619 6.0324 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6.0934 5.6913 6.4309 6.2468 6.0041 5.7697 5.7835 6.0563 5.6744 5.8599 6.2318 6.0063 6.6576 6.2697 6.1273 6.1154 5.9487 5.9332 6.2374 5.922 5.9428 6.0146 6.5416 5.9884 6.1577 6.4913 5.9273 5.8991 5.9509 5.5855 5.7911 5.8005 5.687 5.6942 6.0006 5.9473 5.9144 5.8244 5.8317 5.8094333 5.9415333 6.6416 6.7238333 6.741 6.8550667 6.7057 6.4451 6.7571 6.4694 6.7809 6.3777 6.2268 5.8309 6.257 6.1731 6.4733 6.2659 5.9161 7.2142 6.1269333 6.2734333 5.5877333 5.6559 5.5823333 6.3264 5.9838667 6.0111 6.1431333 6.1343333 6.2918 6.0246 5.8226 5.8338 5.7205 5.6116 5.5184 5.5231 5.6498 5.8821 5.9529 5.9173 6.4292 6.017 5.9253 5.9326 5.8233 5.7424 5.7797 5.8389 5.7346 5.8182 5.8526 5.8006 5.8579 5.8792 6.4034 5.9558 6.0183 6.1685 6.2195 6.1019 6.236875 6.2657 6.221 5.965 6.1711667 6.14275 6.26855 6.00385 6.0897 6.2190333 6.1727333 6.1097 6.261975 6.1269333 6.2734333 5.5877333 5.9838667 6.0111 5.76168 5.5823333 6.3264 6.1431333 6.1343333 5.4712 5.6318 6.0338 5.7915 5.6989 5.5643 5.5407 5.8557 6.1561 6.2869 5.84 5.8916 5.8768 5.906 5.7969 5.9743 6.2192 5.6367 5.9116 5.854 6.2056 6.2167 5.8426 5.7123 5.9677 6.3594 6.4397 5.9516 5.9621 5.9222 5.9632 5.9405 6.1886 6.6634 6.7728 6.0399 6.3667 5.9779 5.687 6.07605 6.5855 5.9538 5.77165 5.9694 6.1528 6.05205 5.7988 6.04915 5.9217 5.8331 6.5529 5.8438 6.0427 5.7387 6.0214667 5.55175 5.4844 5.71975 5.475 5.9801667 5.86735 6.2207 6.2967333 6.1222667 5.8591333 5.7166667 6.0815 5.9034667 5.8110667 5.7187 5.5906 5.7054 5.8177 5.6677 5.8771 5.8881 5.841 5.5011 6.1008 5.9684 6.3709 5.9174 5.6174 5.9503 5.8602 6.1976 5.8115 6.0341 5.7971 5.7494 6.025 6.0025 5.7805 6.2159 6.1345 6.1404 6.2566 6.0483 5.8825 5.9429 6.5865 6.1655 6.1645 6.0306 6.5376 6.2226 6.3436 6.1656 6.1077 5.7871 5.8431 6.0516 5.7875 5.9473 6.0051 6.2423 5.9222 5.8649 +"G507" 9.0886 10.6373333 9.9079 8.47555 9.0248 10.082 10.507 9.3997 10.8233333 9.5274 9.9153 9.3874 8.8595 9.7985 9.3019 9.3351 11.0706667 9.10255 8.8546 9.3972 9.60265 11.17 9.1506 9.2978 9.0024 9.1859667 9.61568 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10.13 10.446 9.9707 10.602 10.081 8.8536 10.523 9.7483 9.7313 9.7283 9.8007 9.0471 9.3552 8.592 8.4322 8.5405 9.2951 9.4827 9.1339 11.611 11.105 11.223 10.055 10.835 11.052 10.56 11.391 10.409 11.3715 11.4005 11.06 10.673 11.31 11.8315 11.0205 10.13445 11.3105 10.892 11.3405 12.673 11.528 8.5793 12.175 11.192 9.5907 9.1681 9.58305 9.4129 9.5686 9.91995 9.3909667 9.76675 9.0711 10.0562333 10.2641 9.8083333 9.8197667 10.6913333 9.7889 11.1199 9.7419667 9.7863 10.408 12.149 11.482 11.582 10.473 10.97 11.117 10.439 9.3597 12.672 11.722 11.637 10.947 10.458 11.278 9.747 10.262 9.8791 10.1 9.7784 9.9264 9.8855 9.3848 9.0024 9.846 10.094 9.8735 10.127 10.058 11.144 9.1422 9.7468 9.466 11.143 10.3945 9.1178 9.1784 10.03 10.812 10.989 9.9306 8.1917 9.6175 9.8436 9.6083 9.6918 9.8626 +"G660" 10.06255 10.23 10.502 10.18225 9.954 10.108 9.9889 9.81 9.8505333 9.8421 9.74925 9.1256 10.3045 9.6702 9.9124 10.1468 10.0788333 10.10405 10.935 10.5585 10.753 10.0026333 10.4475 9.1653 10.564 10.1213667 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5.076 4.5367 6.4723 4.7679 4.617 4.7058333 4.7465667 4.8505 6.17 5.7349 4.6336333 4.707 4.7454333 4.8438333 5.0029 7.1141 7.1199 7.0523 6.5742 6.5237 6.3893 6.2161 6.1493 6.5903 6.4309 6.6703 6.0091 6.7261 6.7204 6.3126 6.3948 6.1411 6.0034 5.9398 5.5687 5.8835 6.1644 6.7746 7.0718 6.6883 6.8112 7.3878 7.0408 6.7477 6.3751667 5.99505 6.2601 6.421125 5.95665 6.18025 5.9466 6.2152333 6.19105 6.50105 6.103 6.4644667 6.4476333 6.1944667 6.2384 6.31965 4.7058333 4.7465667 4.8505 4.707 4.7454333 6.10576 5.7349 4.6336333 4.8438333 5.0029 5.9989 6.0761 6.2531 5.405 5.6498 6.0867 5.7392 5.6971 6.8925 6.8395 6.7949 7.0606 6.3598 5.6438 5.949 5.8406 6.0975 5.833 6.1253 6.8466 6.6733 6.7473 6.5015 6.6679 6.5325 6.7785 6.5419 6.632 6.4465 6.6686 6.3406 6.6337 6.7585 6.9231 7.1992 7.3116 6.21095 6.8181 5.9686 6.62825 6.34735 6.3908 5.8118 6.4443 5.9095 6.20475 5.96845 6.7225 4.952 5.0687 4.2815 5.4861 4.8567 5.3006 7.2329667 7.06195 7.1064 7.564 7.3903 6.8633 6.9164 7.9886 7.2121 7.0548667 6.7851 6.7881333 6.3711333 6.2554 6.22 6.4337667 6.305 6.4027 6.6164 6.4646 6.5414 6.6495 6.7222 6.8381 6.7203 6.7228 6.6131 6.2034 6.1652 6.1616 6.4436 6.8571 6.7729 6.8985 6.6177 6.4649 6.5934 6.5843 6.1105 6.547 6.9773 6.4156 6.5468 6.2322 6.323 6.3425 7.0315 6.5902 6.8968 6.7673 6.8981 6.76855 6.923 7.0806 6.9972 6.5803 6.4896 7.0883 7.042 7.2794 6.6737 6.8872 7.1538 6.8775 +"G753" 9.7444 10.202 9.50615 8.7819 9.4135 9.5755 10.6506667 10.505 10.6943333 10.593 9.53305 8.5751 9.1689 7.9068 8.9138 9.72365 8.0456333 9.44555 8.6076 10.3 10.0555 10.393 9.3371 8.0895 9.6755 9.2587667 9.67994 9.7282 9.12372 10.8385 9.3682 8.143 9.8538 9.7690333 9.4919 9.2678667 8.40085 9.2446 9.66244 7.5692 9.45945 8.5795 7.3649 9.4755 8.6859 8.4959 9.7568 9.6857 9.7287 9.6766333 8.0565 9.57415 9.811925 7.9347667 8.0993 9.4999333 8.96265 8.7602 8.0654 8.6273 8.4367667 8.4959 9.0179 8.55485 8.7299 9.5962 8.0565 9.3371 7.6388 8.9855 9.52155 9.62435 9.4321 10.0835 10.15492 9.2074 9.338 9.1321 9.36185 9.0752 8.0173 9.4604 9.4107333 9.5397 9.689 9.7388 9.6236 9.5389 8.6476 9.6440667 7.8786 7.7638 9.2233 10.156 9.8625 10.0501667 9.7809 8.0311 9.2731 9.1991 9.1268 9.39785 8.9716 8.9961 8.9889 7.1527667 9.0134 9.0737 9.2586 8.8503 9.6898 9.0288 8.6246 9.2525 9.0074 9.7366 8.987 9.2612 9.6304 9.3304 8.4944667 9.1406333 9.6389667 9.5892 8.5217 9.6793 9.4049 10.01 9.704 9.6721 9.3237 9.4624 8.0594 9.4433 9.72635 9.8905 9.8061667 9.6934667 9.6402 9.8586 9.6595 8.4687333 8.1321 9.2894333 9.6279 8.6829 9.613 9.475 9.1563 9.6768 9.3536 8.9501 9.1933 9.2108 9.1561 9.7705 9.9048 8.4421 9.1809 8.8807 8.70335 8.55375 8.17785 8.6326 8.64435 8.55865 8.7165 8.5114 8.2809 9.2727 10.2606667 9.9025 8.3783 8.1675 8.63645 9.3748 9.5452 8.4698 8.3042 9.0569 9.02595 9.1074 9.12565 8.83725 8.4807 8.60745 8.3816 8.15865 8.3761 8.59985 9.1844 8.9862 8.3273 8.3959 8.2002 7.9884 8.0209 8.5466 9.2663 9.3572 8.7492 8.2569 9.1672 9.291 9.1748 9.2773 9.3167 9.2604 9.0883 8.3968 9.1086 9.2176 8.8924 8.5172 9.1059 9.3279 9.0145 8.625 9.6093 9.233 9.5541 9.1693 9.4014 8.7928 9.033 9.8019 10.001 10.552 9.2091 9.2431 9.2947 9.4653 9.183 9.7906 9.174 9.60168 9.35236 9.27572 9.25892 9.7833 10.014 10.014 10.119 9.6701 9.5335 9.3249 9.0518 9.4567 9.8152 9.6635 9.8605 9.7958 9.4286 8.9998 9.1086 9.0172 9.0094 10.147 10.131 10.004 9.7883 9.8145 9.2095 10.017 9.5017 9.7572 9.5041 10.276 10.033 9.9194 9.9645 9.5901 9.8083 9.1197 8.7267 9.6112 9.2776 9.0245 9.2502 9.5651 9.5603 9.3294 9.3159 9.3144333 9.4341 9.2889667 7.9168667 8.0479333 8.0839 8.1765667 8.2047 9.604 9.8376 10.146 10.529 9.8334 9.7433 9.0592 7.9068 7.5692 8.4892 8.6315 7.8882 8.3002 7.9094 8.1775667 8.4576 8.7596333 8.4779667 7.7591667 7.5751333 7.6724667 7.5524 7.2832333 8.5983 8.9023 8.6754 9.4633 9.6664 9.6674 9.4886 9.1981 8.7247 8.8657 9.4833 9.7354 10.106 9.8677 9.5998 9.9198 9.2101 9.4097 9.3519 8.7729 9.2308 9.4064 9.9303 9.9599 9.9719 9.523 9.2283 9.3484 9.2223 9.7910667 9.75415 9.4882667 9.557525 9.7255 9.67185 9.2752 9.5523667 9.65775 9.6967 9.17125 9.6022 9.8777 9.8016333 9.443 9.79085 7.9094 8.1775667 8.4576 7.5751333 7.6724667 8.742 8.4779667 7.7591667 7.5524 7.2832333 10.085 9.546 9.3638 8.5713 9.0532 9.1381 8.9563 8.0069 9.4291 9.4531 9.0696 8.8739 9.4925 9.8223 9.3883 8.997 9.4613 9.5868 9.7634 8.775 9.0468 9.8184 8.2482 8.3927 8.3422 9.4568 9.6488 9.371 9.6592 9.9076 9.8363 9.6927 9.9967 9.757 9.997 9.3683 10.0735 9.98445 9.17705 9.78745 9.92305 9.7762 9.486 9.76835 9.954 9.7454 9.3739 10.03075 8.3161 8.0993 8.0565 8.6824 8.6359 7.5404 9.8491667 9.2292 9.10065 8.85025 8.75765 9.9674333 9.1036 8.8839 9.7319333 9.6734 9.4909667 9.1436667 9.9257667 9.4001333 9.1071333 9.3009 9.4227 9.0575 8.5757 8.7223 9.1461 9.7903 8.9507 8.645 8.1913 8.1831 9.6242 9.0293 8.758 9.0115 9.0141 9.9272 9.7193 9.7697 9.009 9.0007 9.5661 9.5459 9.2555 8.5743 8.1643 9.4251 9.5907 9.2814 9.5359 9.6046 9.9005 9.5372 9.7855 9.4408 9.81505 9.596 9.4828 9.1655 9.4459 9.5439 9.246 8.2338 7.9874 9.4301 9.317 10.215 9.4436 9.7884 +"G754" 9.82255 9.1207333 10.3655 9.4941 9.75305 8.4492 9.5795333 10.4495 9.4628667 10.5455 10.285 8.8044 9.57255 9.8543 9.5733 10.277 7.9669667 9.56465 9.6424 10.632 9.3202 8.2226667 9.5769 9.6713 10.176 9.548 10.4868 10.057 9.6602 10.7655 9.9033 9.5185 10.685 10.1136667 9.7576 10.1126667 9.44535 9.6217 9.5653 8.0561 9.4882 9.8336 9.0991667 10.391 9.1913667 9.5298 9.6751667 9.0269 9.2528 9.9352333 9.4488 9.70105 9.85685 8.8486 9.5447 9.7877667 9.6427 9.3393 9.1517333 9.2268 9.6351667 9.5298 9.882 9.76705 9.6476333 9.7002333 9.4488 9.4354 9.731 9.8734 9.6604 9.64545 9.6586667 10.7292 10.3482 9.9055 9.24342 9.56942 9.7149 9.948 9.9853 10.214 9.8847333 9.9479667 9.8830667 9.9059333 9.8811333 9.9478333 9.9194 10.4063333 9.2237 9.4231 9.60055 9.4433333 9.63 9.4567333 9.8357 9.9282 9.8687 9.4339 9.6932 9.8647 9.9101 10.082 10.209 8.9401333 9.7439 9.6302 9.6203 9.8717 10.008 9.4304 9.323 9.6854 9.7739 10.704 9.9334 10.225 10.096 9.5391 9.5724667 10.1875667 9.8377333 9.9902667 9.819 9.5451 9.8953 9.7868 9.8435 10.066 9.7189 9.7234 10.092 9.9173 10.263 10.106 9.6359 9.8679667 9.8250667 9.9245667 9.9438333 9.4557 9.1436 9.5889667 9.7208 9.4904 9.6454 9.8237 9.3295 9.6384 8.9962 8.91 9.198 9.4555 9.2357 9.8942 9.8725 9.905 9.5876 9.8787 9.6377 9.21635 9.71575 9.54705 9.96425 9.91785 9.6711333 9.4732333 9.5552333 10.1696667 10.3936667 10.18 9.9939 9.6561 9.83035 9.1404 9.7066 9.613 9.3175 10.0066 9.26235 9.56405 9.365 9.2738 9.7132 9.3668 9.3792 9.37755 9.31635 9.3648 9.1172 9.2039 9.353 9.5033 9.8054 9.528 9.8228 9.1008 9.146 9.6618 9.643 9.5581 10.049 10.057 9.9062 10.164 10.35 10.127 10.145 10.175 10.327 10.311 9.9325 9.6431 10.042 10.252 9.9515 9.5109 10.881 10.354 10.791 9.1578 10.219 9.5857 9.5612 10.321 10.513 10.791 10.134 9.902 9.3192 9.4021 9.7675 9.7091 9.3916 10.2208 10.09706 9.29852 9.44814 10.982 10.821 10.953 10.364 10.666 9.3179 9.4747 9.6875 10.037 11.055 10.93 10.469 10.085 9.6617 9.4613 9.7005 9.4691 9.6013 11.032 10.885 10.362 9.7444 10.096 10.033 11.117 10.527 10.396 10.579 11.179 10.109 10.805 11.07 10.077 10.325 9.2607 9.5808 10.016 10.178 9.398 9.5673 10.606 10.724 10.596 10.455 9.8892667 9.6882333 9.7933 9.8903 10.34 10.406 10.3536667 10.3023333 10.429 10.316 10.376 10.805 10.339 10.254 10.521 9.8543 8.0561 9.8735 9.4406 8.9619 9.5053 9.2224667 9.3755667 9.0883 9.3933333 8.9499667 9.2123 9.0390667 9.2901 9.1935 9.2668667 10.011 10.486 10.482 10.389 10.489 10.485 10.201 10.751 10.308 10.287 10.001 9.9719 10.897 10.471 9.7625 10.422 9.6613 10.007 9.9565 10.003 10.041 10.014 10.154 10.515 10.556 10.158 9.8509 10.072 9.7093 11.044 11.0345 10.3958667 10.78 11.063 11.017 10.2505 10.648 10.9395 10.926 10.12255 10.79 11.0736667 10.98 10.1904 10.62775 9.2224667 9.3755667 9.0883 9.0390667 9.2901 9.42706 8.9499667 9.2123 9.1935 9.2668667 9.9391 10.248 9.7924 9.6925 9.9417 9.4122 9.5759 9.5568 10.302 10.584 10.663 10.426 10.658 10.561 9.5341 9.7931 10.448 9.9791 10.224 10.34 10.484 10.427 10.445 10.064 10.046 10.18 10.032 9.4946 10.233 10.379 10.479 10.297 10.75 11.035 10.466 10.625 10.7585 10.7435 9.7371 11.081 11.052 10.861 9.98695 11.084 10.521 10.7915 9.7783 11.002 8.9303 9.5447 9.4488 9.0453 9.1564 8.8046 9.2777 8.65875 9.35575 9.5113 9.08895 9.6575 9.39425 9.4877 10.216 10.1783333 9.5718333 9.1554 10.9493333 10.0013 9.3739333 9.6226333 9.5681 9.7271 9.0728 8.814 9.1375 10.085 9.7494 9.3312 9.312 9.7807 10.037 9.9671 9.5248 9.3441 9.1228 10.668 10.048 10.334 9.4395 9.7024 10.077 10.233 10.247 10.557 10.309 10.176 10.406 9.9201 10.228 10.113 10.926 10.707 10.366 9.8852 10.8835 10.547 10.6735 10.0935 9.9937 9.08 9.1655 9.1741 9.0135 10.079 9.9553 9.6199 10.344 10.202 +"G755" 13.4385 13.284 13.6115 10.716 13.399 13.405 13.356 13.339 13.0666667 13.2745 13.764 12.782 12.906 10.382 12.827 13.486 11.9946667 13.4685 9.6561 13.3065 12.829 12.762 12.931 12.389 12.566 13.4443333 13.487 12.504 13.7148 12.968 11.477 8.791 13.367 12.819 13.25 13.4706667 12.136 12.4655 13.5228 11.713 12.4455 13.051 11.7523333 12.442 11.8396667 12.707 13.5243333 12.036 12.606 13.021 11.962 13.599 12.87725 13.056 12.796 12.9713333 13.355 11.744 12.73 12.745 11.5936667 12.707 12.523 12.605 11.804 12.208 11.962 11.634 11.711 12.571 13.476 13.6775 13.321 13.444 13.6264 12.644 13.1348 13.6406 12.4065 11.199 10.818 13.344 12.9366667 13.049 12.88 12.9133333 12.6673333 12.9983333 10.753 13.5053333 11.094 9.5784 12.564 13.34 13.451 13.3956667 13.488 11.937 12.374 12.699 11.322 12.563 12.688 13.156 12.892 9.5434667 13.094 12.323 13.232 12.57 13.032 12.612 12.242 12.735 12.35 13.337 12.783 13.391 13.219 12.071 10.252 11.7993333 13.5856667 12.5973333 11.717 13.4125 13.146 12.564 12.941 13.028 13.03 13.081 11.864 13.489 13.727 12.94 12.9893333 12.899 12.7766667 12.9646667 13.0213333 12.968 12.176 12.9486667 12.822 13.341 13.475 13.094 12.192 13.281 11.939 11.696 11.957 13.033 13.179 13.181 12.672 11.815 11.866 12.914 12.91 13.2785 12.71 13.204 13.2965 13.058 12.4433333 11.959 12.7806667 12.4606667 11.814 12.6396667 11.319 11.712 11.18 12.287 12.554 10.15 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8.7196667 7.6816 8.4257 7.97735 7.9598 8.1513 9.6732 8.7095 9.43985 10.151 8.3852 8.5008 8.4181 8.9448667 8.7174333 8.8992333 8.0979 7.5164 7.7668 8.8421 8.5467 8.4703 8.6781 8.80655 8.47925 8.396 8.3125 8.17495 8.1821 8.59195 8.33545 8.18535 8.1594 8.30805 8.1804 8.2754 7.5896 8.0382 7.6711 8.2732 7.6111 8.9083 8.2017 8.3335 8.2468 8.069 8.8051 8.5967 8.4522 8.6011 8.7752 8.8105 8.9315 9.2304 8.8414 9.6126 9.4969 9.7355 8.7378 9.4976 9.4697 9.2964 8.9734 8.7944 9.1595 9.2403 8.9301 8.8453 8.8654 8.5648 8.7308 8.7038 8.5017 8.6006 8.7761 8.6546 8.3879 8.5775 8.6455 8.8515 8.70152 8.90532 8.64486 8.704 8.7323 8.7519 8.5336 9.0053 8.9329 9.5604 9.2712 8.8962 8.9652 8.7612 8.8539 8.7191 9.6926 9.3826 9.583 9.5175 9.3489 8.6299 8.6245 8.6869 9.2959 8.3605 8.8593 9.012 8.9322 8.8406 8.875 8.851 8.4161 8.7423 9.045 8.9208 8.7681 9.3407 8.7181 8.7681 8.7775 9.2095 8.5392 9.1091 9.4292 9.3039 9.3615 8.8424333 8.8581 8.9369333 9.9790667 10.2233333 10.3703333 10.2156667 9.9982667 8.4332 8.4231 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10.192 11.0723333 10.7946667 8.8498 10.076 8.9034 12.299 10.5475 11.478 10.9313333 11.4266 12.1102 11.69 10.11316 10.5762 10.23 11.863 9.0247 11.772 10.9643333 11.0883333 11.019 11.1383333 10.999 11.0196667 9.1816 11.5486667 8.9455 8.6955 10.723 11.0426667 11.309 10.9043333 11.441 8.5954 9.8101 10.539 10.976 10.6715 11.77 11.712 11.72 9.0401333 11.679 10.853 12.297 11.083 12.216 11.912 10.679 10.968 10.923 11.848 12.08 11.882 12.124 11.562 10.831 11.01 11.4043333 10.949 8.9772 10.881 11.432 11.387 10.589 10.608 10.935 11.445 9.2296 11.59 12.28 11.3266667 10.6883333 10.5673333 10.4623333 11.0186667 11.1983333 10.9236667 8.9419 10.747 11.837 11.331 12.662 11.9865 9.7836 10.107 10.168 9.9976 10.678 11.58 12.107 11.72 10.7306667 9.1003 9.3722 10.2115 10.755 10.8205 10.873 11.6535 12.0395 10.531 10.8006667 11.029 10.6023333 11.2316667 11.3716667 11.24 8.9615 8.7313 9.0996 10.826 9.9035 10.253 10.629 11.03 10.01325 9.6904 9.7145 9.43385 9.18465 10.351 9.70075 9.11635 9.1572 9.45015 9.8142 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11.44 11.892 11.872 12.221 10.869 11.271 11.269 11.462 11.695 11.87 12.06 12.275 12.149 11.904 11.305 11.254 10.911 11.66 11.171 11.995 11.455 11.629 11.393 11.662 11.523 11.114 11.301 11.398 11.256 11.599 11.458 11.112 11.329 11.719 12.017 11.651 11.55 11.913 11.89 11.694 10.748 10.8163333 11.285 11.3853333 11.299 11.6666667 11.8976667 12.0363333 11.165 10.676 10.151 9.4766 10.712 10.79 10.661 9.1325 8.9336 10.269 10.455 9.592 9.1742 9.8594667 9.7831 10.2513 9.7106 9.6857667 9.1087667 9.6340333 9.9061 8.9999333 8.6792667 10.749 10.592 10.161 11.2 11.57 11.115 11.635 11.079 11.232 11.571 11.845 11.764 11.421 11.296 11.463 11.642 12.124 11.541 11.307 10.244 10.496 10.606 11.361 11.531 11.813 11.471 11.421 11.515 11.583 10.6833333 10.752 11.1386667 10.9795 10.736 10.8905 10.5005 11.0026667 10.559 10.446 10.901 10.5493333 11.0443333 11.0326667 11.089 11.225 9.8594667 9.7831 10.2513 9.6340333 9.9061 9.7481 9.6857667 9.1087667 8.9999333 8.6792667 11.229 11.119 12.608 11.509 11.281 12.451 11.5 9.3937 10.871 11.157 11.341 11.43 10.951 10.74 11.091 11.001 10.641 10.977 10.613 10.578 11.353 10.349 11.138 11.405 11.561 10.154 10.141 9.7958 11.351 11.512 11.458 11.202 11.586 11.389 11.606 11.758 10.7985 11.3525 10.532 11.163 10.8745 11.2925 10.6585 11.72 10.859 11.1695 10.812 11.395 11.393 10.658 9.0278 10.923 10.325 10.241 10.9913333 10.9765 10.661 11.322 10.994 11.0843333 10.9315 11.062 11.1653333 11.1936667 10.9803333 11.1086667 11.2983333 11.061 11.4443333 10.8153333 10.747 11.476 11.811 11.074 10.822 11.024 11.667 12.14 12.052 11.466 11.417 11.18 11.123 11.24 10.937 10.861 11.109 11.058 11.301 11.51 11.029 11.207 11.445 11.044 10.593 11.098 10.994 11.115 11.053 11.014 11.136 10.476 10.867 11.413 11.0465 11.1565 11.3795 11.8375 10.901 11.563 10.905 11.915 11.083 10.698 11.257 9.5857 10.969 9.9756 +"G847" 7.64595 11.9626667 11.135 9.72755 11.7705 7.9914 12.269 9.72685 9.9564333 9.3535 11.2695 8.697 11.9685 10.933 8.4656 12.075 9.8678 12.289 10.998 11.683 8.3586 7.5470667 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8.2136333 8.36905 8.4071 8.7050667 8.7227 8.5693 8.3140333 8.5031 8.5444333 8.3860333 8.2804667 9.0939 8.9135 9.6179 8.4393 8.4886 8.9453 9.0313 9.0255 8.1746 8.2262 8.8783 8.669 9.0533 8.7988 8.3251 8.6273 8.8547 8.7866 9.0295 8.8513 8.6082 8.5178 8.3181 7.9404 8.1543 8.9164 8.5241 8.5248 8.4722 8.5504 8.6729 8.5468 8.774 8.3991 8.6988 8.7977 8.65175 8.4513 8.2533 7.8507 8.0102 7.9877 8.0709 8.3103 7.3721 7.8803 7.7782 7.9468 +"G856" 11.4805 12.1 10.5365 8.56825 10.7055 12.923 12.5713333 11.577 12.484 11.8275 10.6235 10.923 10.272 9.1986 12.381 11.8355 8.8719667 10.969 7.6857 11.816 11.1735 12.295 10.4585 8.9859 10.056 10.5333333 11.492 11.874 10.0326 13.211 8.6772 8.7718 11.578 12.0333333 11.326 12.0933333 8.48825 10.4605 11.7622 8.627 9.24065 10.438 9.9986667 11.759 8.2108333 9.1114 11.936 10.722 11.327 11.296 8.7753 10.658 12.15925 9.7679 9.8076 10.992 11.3275 9.9918 11.9783333 10.564 8.7727667 9.1114 10.463 11.1555 10.29 10.9763333 8.7753 11.297 8.4923 9.7313 11.9805 11.925 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11.214 12.1065 11.741 11.6925 11.145 11.675 9.9589 10.189 11.994 11.988 11.71 12.853 12.253 11.981 11.855 +"G857" 10.10505 10.1076667 10.551 10.742 10.169 9.4914 9.0341 9.3191 9.1789667 9.18835 10.4695 9.6997 10.05685 8.8008 10.442 10.469 9.1921667 10.263 8.737 9.7587 11.3145 10.0963 10.163 9.3052 11.286 10.0246 10.5396 10.429 10.6218 9.2638 10.125 8.7592 10.223 10.8196667 10.551 9.9882333 9.7442 9.48125 10.2504 8.7163 9.6681 10.228 8.965 10.541 9.9404333 9.7068 10.091 10.462 10.544 10.0139667 9.5648 9.68415 10.64625 9.0264333 9.6816 10.151 10.17365 9.7238 9.1834333 9.5803 9.6491 9.7068 9.788 8.9928 9.7243667 10.0582 9.5648 9.791 8.8168 11.039 9.9534 10.092 10.2556 10.4212 10.07596 10.046 11.1122 10.6584 9.5048 10.893 9.3224 10.16 10.7123333 10.5316667 10.6273333 10.592 10.9376667 10.5733333 9.3067 10.188 9.8044 9.0798 9.51715 10.1416667 10.129 9.7982333 10.166 9.2829 9.7916 9.7614 9.8887 9.76255 10.514 10.554 10.588 8.7422667 10.311 10.785 10.18 11.274 10.058 10.516 11.203 10.969 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8.2034 8.2065667 8.420175 8.2521 8.1976 8.22685 8.2333667 8.24905 8.20195 8.2753 8.2743333 8.2004333 8.1254 8.2108667 8.3239 7.6000667 7.6434 8.1271333 7.8149333 7.8105667 8.43246 8.3424 7.6775 7.6197333 7.7989 7.9196 7.9976 8.277 7.9256 8.0463 8.0953 8.2182 8.3154 7.8403 8.0251 8.1207 8.074 8.2975 8.006 8.0895 8.2257 8.5227 8.0451 8.3601 8.3851 8.3126 8.1257 9.0779 8.9227 9.0412 8.0617 8.0649 8.4273 8.3375 8.3847 8.165 8.2141 8.9446 7.7333 7.9262 7.9322 7.88475 8.02195 8.0084 8.16935 8.12295 8.19405 8.24545 9.12095 7.97145 8.13325 8.1011 7.8883 7.9209 8.1913 8.6531 8.2318 8.0267 8.3103 8.4577333 8.2264 8.5174 8.5814 8.44635 8.4855333 8.5252 8.6106 8.2405 8.377 8.2996333 8.4664 8.1731667 8.4849 8.3149333 8.4868333 7.8909 7.6318 8.1369 8.2647 8.1874 7.6363 9.3196 9.9405 9.5411 9.8471 7.8356 7.7158 7.8907 8.1459 8.1285 8.7258 8.5215 9.0859 9.2958 8.8739 7.7962 8.0302 7.9891 8.1051 8.1319 7.9093 7.9937 7.9872 8.1044 8.1442 8.2175 8.1101 8.0638 8.292 8.42785 8.922 8.9841 9.1518 8.3704 8.1613 8.162 8.3688 8.1767 8.2032 8.1749 8.2662 8.1571 8.0431 +"G879" 10.862 9.8906333 10.437 10.0805 10.6485 11.888 9.5769333 10.981 9.8173333 10.7605 10.385 10.47 10.354 9.4111 11.352 10.6165 8.9628667 10.5755 9.8154 10.736 10.754 10.1826333 10.488 9.7168 10.117 10.557 10.727 10.919 11.0002 10.4195 10.491 9.8153 10.68 10.884 10.275 10.31 10.236 10.088 10.8758 8.6787 10.0915 10.901 9.0223667 10.782 9.9441333 10.196 11.1693333 10.103 10.863 10.9146667 9.7731 10.8105 11.02225 9.6470333 9.4963 10.8863333 10.779 10.065 11.444 10.816 10.5283333 10.196 9.9769 11.0115 10.3326667 10.7916667 9.7731 10.324 9.1336 10.761 10.683 10.554 10.5623333 10.7872 10.2758 10.803 11.1942 10.7048 9.8338 10.425 9.558 10.857 10.7426667 10.7046667 10.6603333 10.7433333 10.7046667 10.7063333 10.654 10.637 10.13 9.5237 10.1575 10.823 10.662 10.419 10.764 9.9464 10.403 10.109 10.39 10.593 10.861 10.801 10.975 8.8912333 10.788 11.172 11.059 11.173 11.198 11.121 11.234 11.033 11.088 10.772 10.617 10.91 11.019 11.14 10.8936667 10.7253333 10.7356667 10.3259 10.377 11.13875 10.731 10.451 10.773 11.123 10.856 10.652 9.5837 10.922 10.4675 10.9893333 10.7723333 10.9076667 10.8003333 11.0213333 10.877 10.0426333 9.628 10.662 10.448 10.898 10.753 10.6795 10.207 11.612 10.594 10.856 10.854 11.058 11.286 10.699 11.2486667 9.9784 10.378 10.937 10.754 10.448 11.361 10.915 11.0595 11.166 10.8413333 11.1633333 10.804 11.1313333 10.9376667 10.8046667 10.22 10.21 10.35 10.337 10.434 10.215 10.688 11.174 10.881 10.535 10.4235 10.451 10.5535 11.2135 10.831 10.494 10.5615 10.555 10.346 10.003 10.206 10.383 10.224 10.505 9.9794 10.654 10.481 10.246 10.123 10.036 12.091 12.012 12.08 12.083 12.096 12.125 12.143 12.052 12.062 12.414 12.506 12.562 12.021 12.261 12.372 12.716 11.046 10.73 11.001 11.207 10.78 11.412 11.494 10.448 10.293 10.231 10.935 10.951 10.237 10.344 10.653 10.502 10.358 11.3684 11.2424 11.203 11.2906 11.136 11.034 11.062 10.914 11.291 11.224 11.69 11.524 11.271 11.125 11.107 11.178 11.145 11.464 11.531 11.763 11.721 11.74 10.81 10.494 10.532 10.814 10.445 10.665 10.714 10.635 10.625 10.502 10.794 10.559 10.932 10.977 10.619 10.685 10.873 10.563 10.58 10.822 10.769 10.351 10.722 10.645 10.758 10.849 10.7106667 10.7336667 11.0986667 10.8286667 10.793 11.0176667 11.1663333 11.263 10.669 10.855 10.573 10.607 10.51 9.7583 8.9543 9.4111 8.6787 10.064 10.098 9.47 9.0247 10.0017 10.21 10.2806667 10.3903333 10.2806667 9.8937667 10.1053333 10.1765333 9.7362667 9.6849667 10.354 10.515 10.47 11.446 11.672 10.251 10.602 11.555 11.388 10.559 10.865 10.728 10.598 10.607 10.713 10.675 10.921 10.521 10.501 10.229 10.14 10.287 10.814 10.719 10.824 10.881 11.299 11.092 11.383 11.261 11.258 11.0943333 10.9915 11.2525 11.308 11.0165 11.036 11.197 11.1265 11.1475 10.9036667 11.272 11.2106667 11.0173333 10.8985 10.0017 10.21 10.2806667 10.1053333 10.1765333 10.4206 10.2806667 9.8937667 9.7362667 9.6849667 10.167 10.144 10.482 9.8626 10.036 10.298 10.289 9.402 12.09 12.374 12.481 12.555 10.949 10.745 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5.8382 5.3344 5.5576 5.45 5.6122 5.2323 5.502 5.1174 5.8711 5.6878 5.2904 5.6218 5.5869 5.3809 5.5557 5.4853 5.5583 5.3939 5.7007 5.0105 5.2194 5.0202 4.5074 4.4753 3.9481 5.7823 5.4528 5.5266 5.8562 5.6141 5.972 5.6248 5.44678 5.233 5.44524 5.84476 5.6069 5.2966 5.2524 5.2083 5.6119 5.5451 6.0644 6.0148 5.5916 5.9548 5.4049 5.3861 5.7715 5.8643 6.6586 5.7111 6.5463 6.1166 5.3308 5.4154 5.5042 5.522 5.5461 5.5654 5.2289 5.5837 5.2239 5.8808 5.6838 4.9094 5.8829 5.474 4.9847 5.4037 4.8743 5.2301 5.5085 5.2949 5.4398 5.8055 5.2363 5.2525 5.2292 5.4866 5.5638 5.7771667 5.6688667 5.7065333 5.6235 5.6308333 5.6793667 5.6610333 5.3597 4.7739 4.2586 4.2359 4.1814 3.8592 3.888 5.1636 4.9576 4.5574 6.4852 4.8629 4.8425 5.3674 5.315 6.3311667 6.3738333 6.4799333 5.9604 5.9066667 6.0137 5.3874333 5.3044333 5.7281 5.9285 5.4051 5.7139 5.3965 5.3867 5.1601 5.3941 5.5131 5.6383 5.386 5.4099 5.6804 5.6129 5.422 5.2816 5.0686 5.2437 5.7653 5.1856 5.0038 5.4311 6.092 6.3677 6.5773 6.8286 6.2035 6.4256 5.7669 5.4897667 5.61695 5.5215333 5.53835 5.7042 5.3975 5.5799 5.4994 5.6644 5.4986 5.78315 5.7147333 5.7814 5.5835 5.589 5.419675 5.3674 5.315 6.3311667 5.9066667 6.0137 6.55044 6.4799333 5.9604 5.3874333 5.3044333 4.9875 5.0252 5.2714 4.8791 4.7597 5.0547 4.9496 4.9691 5.547 5.4265 6.0423 5.9818 5.1673 5.0725 5.2119 5.191 5.4318 5.382 5.514 5.619 5.3659 5.1751 5.5073 5.6591 5.233 4.5337 4.2469 5.195 4.8488 5.2629 4.8723 5.4891 5.1885 4.6829 4.7749 4.6469 5.38825 5.61585 5.269 5.5526 5.18345 5.63105 5.10185 5.3533 5.41235 5.3117 5.21505 5.41545 4.419 5.0371 4.9989 4.9721 5.6528 5.7145 5.6397 5.8368 5.75505 6.19355 6.2841 5.7158333 6.13495 7.3525 6.5042667 6.1825 6.4006333 6.1264 5.3856667 5.7063667 5.8832 6.3737 4.8098 5.3435 5.3569 5.5262 5.2642 5.3423 5.2815 5.1676 5.0933 5.6774 5.62 5.27 5.4836 5.5796 6.2678 5.0901 5.3965 5.4035 5.3503 5.0774 5.4944 5.3855 5.4159 5.9712 6.297 5.6841 5.4667 5.7037 5.546 5.5888 5.7113 5.6759 5.6074 5.952 5.53425 5.42115 5.7643 5.58845 5.4849 5.3889 5.3507 5.3301 5.4229 5.5751 5.3642 4.6967 5.2753 5.0673 +"G890" 11.3735 12.4626667 12.482 11.5985 11.7875 9.8509 12.435 12.83 12.3953333 12.861 12.339 11.406 11.25 8.4596 10.931 11.276 12.7316667 11.8205 10.105 12.505 11.5215 11.1796667 11.245 8.2579 10.417 11.0336667 12.6164 11.761 12.0314 12.453 10.655 8.6525 11.915 11.7276667 10.801 12.9856667 9.8892 10.768 12.1218 8.3374 10.519 10.94 9.7876 12.233 9.6538 9.8684 12.205 10.924 10.312 11.693 8.7904 11.377 11.705 9.2087 8.206 11.6603333 11.713 9.1109 10.0795 10.981 9.8389333 9.8684 11.582 9.73385 10.5806667 10.6443333 8.7904 10.963 8.8684 10.711 12.1985 12.226 11.341 12.3812 12.255 11.754 11.0664 11.142 10.7005 11.842 9.0228 10.956 11.4303333 12.1253333 11.932 11.7263333 11.505 12.0326667 12.142 13.1916667 9.0938 8.7236 10.6595 11.836 12.247 12.2426667 12.262 9.0128 10.698 10.955 10.889 11.105 10.771 11.079 10.551 8.8282 10.267 11.517 10.838 11.264 11.889 10.828 10.503 11.304 11.193 12.576 9.6285 11.768 11.245 10.43 9.5638667 9.7471 12.4163333 10.0246 9.9531 11.56125 11.689 12.059 11.626 12.211 12.132 11.728 8.6689 11.049 12.5815 12.03 11.4623333 11.4313333 10.8683333 11.8433333 11.8276667 10.2250667 8.3035 10.2593333 12.222 11.592 11.151 11.72 9.7172 12.491 10.005 10.214 10.205 11.376 10.809 11.879 10.4246667 9.2905 12.146 10.228 10.4865 10.3125 11.3335 10.9815 11.4135 11.792 11.0623333 10.3816667 11.3596667 10.4066667 11.2123333 11.035 9.183 8.5416 8.63535 10.979 9.8336 11.763 11.03 12.1505 10.6415 10.6945 10.4735 10.233 10.3045 10.8715 10.1455 9.92445 9.99805 10.384 10.271 9.9899 10.155 9.7238 11.247 10.138 10.919 11.014 9.2908 9.686 9.663 10 12.266 12.323 11.958 12.313 12.267 12.314 11.777 11.795 12.351 12.081 11.679 12.221 12.272 12.369 11.974 11.706 12.528 12.4 11.704 10.866 11.673 11.935 11.269 11.712 11.046 10.572 11.262 10.797 10.506 10.365 11.342 10.777 10.028 12.1684 11.4852 11.6556 12.0358 12.839 12.602 12.719 12.38 11.396 11.725 11.23 11.145 11.405 12.845 12.697 11.596 11.251 11.987 11.548 11.161 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8.5026667 9.3909 7.7748667 8.0053333 9.2224 8.9363 7.5212 8.309 8.0637 10.942 11.024 7.6645 8.0822 8.169 9.3195 9.393 7.736 7.9412 8.0895 8.1821 10.947 7.6053 8.0537 7.7745 8.1077 8.3012 7.9816 8.0521 8.1729 8.166 8.0974 7.4829 7.5999 8.0086 7.9576 8.138 7.8427 7.9621 8.0165 8.4174 7.92855 7.66055 7.7267 8.1862 7.9474 8.3087 8.6909 7.9536 7.7989 7.8931 7.905 7.823 7.6238 7.7695 +"G897" 10.7045 11.2663333 9.75775 11.9135 11.517 7.868 11.4126667 10.9705 10.5153333 10.251 9.7333 11.341 11.529 9.3277 8.575 11.8725 8.5159 11.173 10.765 11.562 8.4439 9.4605333 11.819 8.992 10.958 11.195 9.6837 11.383 8.58512 9.8514 11.786 9.7644 10.696 11.9776667 9.4842 9.0463 10.5645 10.88 11.4706 8.8386 11.507 10.402 9.4811333 9.2059 9.0289667 10.902 10.3713333 9.6294 10.68 11.889 10.015 9.6881 11.279 9.5583 11.18 11.7483333 9.33945 11.065 9.9723667 10.469 8.8955 10.902 10.386 9.33525 10.4273333 9.5414 10.015 10.328 9.5787 11.322 11.0775 10.9375 11.4056667 9.52966 10.04662 11.058 8.32386 9.18192 8.41695 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6.9088 7.7049 7.98765 8.0879 8.0335 8.3485 7.6292667 7.62855 7.9319 6.9235667 7.3067 7.1350667 7.0260333 6.916 7.0711667 7.021 6.7560667 7.3073 7.2459 7.0664 7.1379 6.9016 7.039 6.9001 7.3208 7.4884 7.3734 7.1354 7.1005 7.1264 7.3416 6.9747 6.9679 7.2353 7.2343 7.0779 7.0642 6.9112 7.141 7.2308 7.7224 7.733 7.0837 6.9898 7.056 7.1723 7.1644 7.2604 7.2413 7.5394 7.2964 7.21765 7.0399 7.30425 7.52685 7.2725 7.1927 7.1119 7.3449 7.2481 7.3613 7.5573 6.8389 7.1285 7.0024 +"G999" 12.192 13.4093333 13.4385 12.6535 13.12 11.34 13.2473333 12.9175 13.1406667 13.0925 13.337 12.725 13.318 10.225 13.091 12.1705 14.423 13.0535 12.757 13.0895 11.5165 13.646 13.037 10.628 12.176 12.9976667 13.4774 12.549 13.6584 12.7155 12.736 11.484 12.799 13.155 13.01 13.743 11.888 12.9505 13.3982 10.026 13.4205 12.499 11.4433333 13.415 11.407 12.44 13.181 12.077 12.095 13.5626667 10.037 12.585 12.9075 9.9487333 11.951 12.9656667 12.8105 12.192 12.5913333 13.227 11.6103333 12.44 13.561 12.4085 13.1903333 11.89 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4.8369 5.8941 5.7336 5.2549 5.612 5.4297 5.2529 5.1500333 5.16835 5.4449 5.9627 5.5118 5.1691667 5.28 5.7447 4.9579333 5.1778667 5.0357333 5.0787667 5.0561667 5.1745333 5.0335 4.9840333 4.9482 4.8928 4.8235 4.8867 4.763 4.8661 4.734 4.9992 5.2037 5.152 4.9195 4.7939 4.7497 5.1407 4.6533 4.7262 4.9547 4.7533 4.7289 4.8733 4.6598 4.8138 4.8644 5.237 5.5428 4.7823 4.7301 4.8213 4.9394 4.8751 5.057 5.2318 5.1982 5.1088 5.1831 4.97925 5.07165 5.281 4.9357 4.9514 4.8621 5.3897 5.0195 4.8875 5.044 4.7541 4.7851 4.7376 +"G1026" 8.9156 9.8476667 8.89945 9.50265 9.4645 10.308 9.937 9.653 9.3252667 9.33825 8.9285 10.673 9.4948 7.8473 10.187 8.9015 10.1966667 9.4467 9.4987 9.2719 9.7855 10.5023333 9.5743 8.8772 9.3921 9.4250333 9.51148 9.423 8.93722 9.2138 9.7131 9.5248 9.2982 9.8300333 9.7231 9.1621333 9.41715 9.1663 9.64542 8.5768 9.5536 9.1587 8.7871 8.7965 9.4892667 9.0922 9.4493667 10.107 11.112 9.6309333 8.433 9.0465 9.5997 8.3654667 10.251 9.5831333 9.63255 9.722 10.686 9.5567 9.0972 9.0922 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8.6568 9.0418 9.477 9.7994 10.041 9.9771 10.351 11.019 10.711 11.064 9.2943333 9.037 9.1021333 8.9281 9.19505 9.17975 8.77745 8.9393 9.10365 8.98655 8.97155 8.9126 9.3764667 9.1967 9.1572 8.96305 9.4101667 9.3486333 9.4612333 9.8229 9.8319333 9.40638 9.616 9.3893 8.9013 9.0672667 8.994 8.7515 9.6909 9.6575 8.9572 10.17 10.172 8.9527 9.4221 9.3269 9.369 9.3202 9.0706 8.6208 8.65 8.9799 8.857 8.8753 10.024 9.2019 9.6696 10.248 8.8325 8.8254 8.8965 10.246 10.087 10.511 9.1323 9.3453 9.1479 9.3283 9.0079 10.399 10.261 10.332 9.34855 10.2185 9.47555 9.85295 9.20855 9.8566 9.51835 9.53915 9.13625 9.69765 9.5183 10.04265 9.0374 10.251 8.433 9.5577 9.447 9.7144 9.6601 9.80055 9.2336 9.80645 9.38815 9.5889333 9.4524 9.5169 9.3905667 9.0768 9.3195333 9.2656667 9.8684333 9.1761667 9.2904333 9.1723 9.368 9.2461 9.6755 9.4916 9.7505 9.2369 9.3146 9.313 9.2634 9.187 9.0105 9.1472 9.1756 9.2454 9.1984 9.6672 9.3491 9.6527 9.6897 9.4642 9.0526 9.1917 9.1709 9.1065 9.2611 9.1879 9.2528 9.2361 9.0344 9.0844 9.4989 9.1593 9.5479 9.5771 9.4758 9.2184 9.2691 9.28535 9.4418 10.199 10.275 9.9167 10.252 9.5927 9.7637 10.794 10.572 11.004 +"G1027" 12.769 12.544 7.94095 8.11745 11.803 9.2946 12.5553333 12.5535 12.1356667 10.2945 7.92865 8.5971 10.987 8.9002 6.8799 13.062 7.8728333 11.673 8.55 11.2885 9.74975 10.9343333 11.2015 9.089 8.2771 10.472 7.69068 8.6548 7.43488 9.1982 8.1589 8.9767 8.6105 13.1043333 8.5411 8.5054 8.08595 8.017 12.0056 9.4535 7.98605 9.2631 9.6678333 7.9853 8.1311333 8.0567 11.7556667 8.9598 8.8565 13.238 8.8472 8.63355 12.4645 12.7766667 8.399 12.9483333 8.3547 8.6871 7.7567 8.4236 9.8965667 8.0567 8.1831 9.54685 8.0032667 8.2377 8.8472 8.2319 9.255 8.0954 11.471 12.2815 11.2163333 7.9751 8.39224 8.6684 7.61824 7.64576 10.762 8.0613 9.3732 9.5508 12.9223333 12.8123333 13.0253333 13.107 13.4403333 12.9986667 8.1509 8.151 8.5266 8.9503 7.9252 11.4293333 12.305 11.6313333 12.064 8.5943 8.0128 7.9586 8.2511 8.07505 9.0114 9.5576 9.7238 9.5428333 8.4008 8.0854 9.6776 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7.4437 7.2954 7.0367 7.45025 7.33175 7.2489333 7.21716 7.15682 6.9701 11.327 8.66718 8.411 6.8679 7.4739 6.9235 7.5557333 6.9621333 7.1113667 7.2159667 7.648 7.1734 7.6388 6.8937667 8.0519 7.8342 6.81345 7.8024 7.3913 11.014 7.2507 7.5433 9.0857 9.7198 7.1122 7.70645 6.8985 6.808 6.8978 7.7615667 6.911 7.0457 6.9684 7.0338 7.0181 7.009 7.3023 7.0409 7.2145 6.8613 7.2788 7.0142 6.8953 7.3101 7.5056333 8.0671667 7.3681 7.1723333 7.7389 8.025475 6.9052 6.9767 7.4555 6.8882 7.0926 6.9563 7.3593 7.1475 7.4317 6.9219 6.9438333 9.0271333 8.6459 7.8972 6.8968667 7.4810667 7.8292 6.9854667 7.0778 7.1609 6.976 7.0489 8.3771 7.1054 8.6415 7.0997 7.0016 7.245 7.1051 7.0639 9.5973333 7.108 7.2964 7.40185 7.545 7.8229 7.5898 7.6575 7.57345 7.448 6.8497333 6.8015333 6.7498333 7.1361 7.1284333 7.1969333 7.5438 7.5284 7.42655 7.2565 7.2954 7.5474 7.357 6.9172 9.85115 9.8581 9.5978 9.1683 8.27795 6.9856 7.46635 9.11265 9.70645 9.41035 10.142 10.049 8.9736 7.4144 7.9988 7.2538 8.1719 7.4486 9.0702 9.9236 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6.7891 6.8889 7.0878 7.1892 6.969 7.0573 7.2696 7.1946 7.0364333 7.1277 7.20615 7.33275 7.1374 7.1870667 7.2972 7.42105 7.22225 7.2323 7.2137667 7.2272 7.0248667 7.1819 10.4169 9.7705333 8.6211667 10.386 8.2139 10.7402 10.1601333 7.9274333 8.7859667 8.6094333 8.3867 6.9557 7.0961 6.8022 6.866 7.2565 6.9537 6.9329 7.142 7.1905 7.2742 7.1429 7.1875 7.1465 7.3914 7.0531 7.3597 7.1881 7.2427 7.3431 7.344 7.0681 7.8366 7.9128 7.7347 6.904 6.75 7.0516 7.1133 6.9843 6.8874 6.8481 6.8595 6.8819 6.8428 6.7561 6.7211 6.986 6.6551 7.0775 6.8713 7.0052 6.5983 7.0149 6.7418 7.1736 6.7764 6.8966 9.2304 7.0773 7.517 10.924 8.9992 8.1806 7.2752 7.1902 7.58765 8.25995 7.55995 7.2806 7.5016 7.8843 7.3220333 7.2422333 7.1008 7.3013333 7.4372333 7.2592 7.3919667 7.4477333 7.2702 6.969 6.8108 9.4519 9.106 7.036 6.8788 6.7887 7.8749 7.396 6.9795 6.9639 6.9521 7.0822 8.4871 7.0007 7.1573 7.0418 7.1188 7.1797 6.9811 6.9776 7.0391 7.1739 7.5891 6.9224 7.1201 6.9801 7.0496 7.0874 7.3697 7.3184 7.4733 7.1235 7.48225 7.15315 7.3022 7.4037 7.148 7.0238 6.924 7.0905 6.9288 7.2828 6.9262 7.209 7.0709 7.0908 +"G1192" 10.0632 9.6758667 11.0595 9.8228 10.10435 9.8571 9.7698 10.931 9.4619 11.1095 10.8055 10.45 10.00565 8.577 9.8274 10.35 10.0012 10.3105 8.7924 11.0165 9.53075 9.14 10.1775 10.165 10.022 10.1584667 10.917 10.82 10.3406 10.7845 8.9612 9.345 10.787 10.646 9.6508 10.8736667 10.66 9.5901 10.4684 8.5391 9.89575 9.4026 8.4852333 9.7571 10.8996667 10.55 10.3946667 9.3238 9.7216 10.533 10.574 10.4895 10.343 8.6678 10.032 10.3793333 9.6982 9.5327 10.166 9.2176 8.6222 10.55 9.3872 9.78255 9.8597 9.7008 10.574 9.829 9.2595 9.6878 10.432 10.2485 10.1071667 10.6678 10.79 10.19 9.52336 10.2518 9.56575 9.7091 9.3862 10.197 10.4013333 10.586 10.392 10.3956667 10.331 10.5906667 10.096 11.1286667 10.695 10.025 9.59655 10.57 10.642 10.6266667 10.608 10.31 10.184 10.685 8.7233 10.08625 9.2886 9.7804 9.8481 7.9416667 9.9043 9.7064 10.157 8.8366 11.072 9.9367 9.2736 9.7985 9.238 10.844 9.804 10.572 9.8612 10.377 8.647 9.1827 10.5226667 9.5435 10.712 10.36475 10.234 10.218 10.467 10.628 10.659 10.208 9.9004 10.416 11.084 10.381 10.4356667 10.5186667 10.477 10.385 10.3356667 9.071 11.182 9.4409333 10.341 9.6209 10.273 10.518 9.5113 11.844 10.23 9.9539 9.7114 9.3587 9.3741 10.428 9.6349 10.207 9.6114 9.9011 9.4151 10.015 10.0272 9.73785 10.2455 10.6525 9.9152333 9.4449333 10.0475 9.6835667 10.541 10.3713333 10.066 10.714 10.269 10.31 10.04 9.9829 9.8679 10.5395 10.561 10.874 10.7805 11.1235 11.64 9.3906 9.44805 9.9801 9.92465 10.308 9.6826 9.8738 10.554 10.449 9.9149 9.292 9.476 9.6864 9.6017 10.301 10.912 10.44 10.562 10.544 10.579 10.805 10.79 10.687 10.449 10.435 10.925 10.844 10.625 10.329 10.669 10.647 10.464 10.029 10.921 10.766 10.36 10.56 10.681 11.089 11.2 11.861 11.06 11.298 10.166 9.6365 9.3088 9.414 9.8479 10.11 9.4497 11.288 10.7034 10.27184 10.4446 11.21 11.382 11.379 11.157 10.951 10.288 10.33 9.6691 9.3592 11.19 11.029 11.122 10.605 10.993 9.7044 9.727 9.7771 9.4795 11.22 10.781 10.646 10.609 10.255 10.435 10.958 10.725 10.39 10.708 11.354 10.447 10.761 11.186 10.239 10.487 9.9753 9.4588 10.076 10.264 9.8747 8.8419 11.017 11 10.914 10.692 11.0023333 10.451 11.071 11.662 11.5746667 11.5996667 11.264 11.079 10.842 11.923 11.92 12.008 11.505 11.012 10.211 8.577 8.5391 8.912 9.0556 9.4151 8.778 9.0591667 9.2909333 10.2276667 9.6968333 9.5409333 9.1666 9.6 9.7563333 8.8571667 8.5202333 8.4923 8.7925 8.5991 9.6205 9.5204 9.7082 9.5746 9.9982 10.899 10.395 11.318 9.8674 10.792 10.442 9.4356 10.285 9.2416 9.775 9.534 8.5781 8.9624 9.343 10.018 10.279 10.132 10.417 10.514 10.278 10.579 11.508 11.5315 10.8583333 10.83225 11.4365 11.5305 10.666 11.0576667 11.467 11.3565 10.5715 11.0553333 11.538 11.361 10.761 10.9255 9.0591667 9.2909333 10.2276667 9.6 9.7563333 9.54542 9.5409333 9.1666 8.8571667 8.5202333 9.7375 9.4317 9.681 9.5351 9.5927 9.225 9.9034 8.4822 10.984 11.111 10.811 10.806 10.59 10.943 10.64 9.6185 10.753 10.197 10.902 9.9309 11.041 11.377 10.765 10.445 10.486 12.262 12.123 11.975 10.172 10.581 10.486 10.523 10.905 11.629 11.578 11.576 11.104 11.2455 10.4945 11.049 11.4065 11.215 10.64 10.914 10.8925 11.1835 10.3055 11.269 8.8237 10.032 10.574 9.3377 10.735 10.76 10.3633333 8.57475 9.5439 9.93885 9.78495 10.6713333 9.63555 9.3998 11.1416667 10.805 9.7747667 10.0587333 11.297 10.5543333 10.0119667 9.7389333 10.454 10.642 10.062 9.8074 10.061 10.692 10.832 10.829 9.4352 10.704 10.953 10.824 10.23 10.171 10.196 11.574 10.768 11.024 10.257 10.606 11.076 11.242 11.051 11.079 10.644 11.276 11.344 10.857 11.082 10.791 11.367 10.883 10.748 10.389 11.3335 10.9845 11.162 10.357 10.417 9.8317 10.016 9.5775 10.185 10.538 11.769 11.702 11.991 12.013 +"G1193" 7.67895 8.4648667 7.9336 8.8203 8.1581 8.1509 8.3488667 8.132 8.3538333 7.77475 8.237 8.6583 8.43925 8.4696 8.2912 7.7129 8.3397333 8.28265 8.2069 7.68985 9.1534 8.3609667 8.5124 8.3927 8.144 8.1268 8.3324 7.8639 8.15108 7.4278 8.0638 8.0648 7.9801 8.4216 8.1904 7.9065 8.80845 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9.2324333 8.26015 8.5162 8.03955 8.9273 8.2248 8.54715 8.8261 9.5156 8.1429 7.58242 8.6356 8.37464 7.4945 9.5266 8.9696 8.6705 9.3176 8.9704 8.1992333 9.68365 9.08885 8.27656 8.225 9.34475 9.1668 8.1913667 8.5748 9.7275667 10.024 8.3060333 9.2057 9.7237 9.4101667 9.0038 9.4593 9.1421 8.4894667 9.9837 9.2653 8.81515 9.63 9.1382333 9.0988 8.1438667 10.024 8.3213 7.54175 9.2786 8.5589333 9.0038 9.1844 7.7848 9.841 8.2293 8.05255 8.4983667 7.3632 7.70408 8.5824 9.1839 9.00202 8.00545 9.459 8.6151 8.9117 9.6089 9.2865667 9.7150667 9.4985 9.744 9.308 7.8001 8.1109333 10.515 8.916 9.24715 8.4294667 8.0229 8.0604333 8.0463 9.8374 9.3045 9.7418 8.7061 8.97975 9.0094 9.044 8.6071 8.0401 9.3862 9.3438 8.9459 9.0013 8.3392 9.1615 8.2484 9.4923 8.8827 8.1569 9.4942 8.8012 8.82 8.0277 8.1271 8.3484333 8.3435667 9.0974333 9.6286 8.8957 9.4996 9.7324 9.5665 9.2563 9.0725 9.1654 9.9736 9.419 8.4179 9.0393 9.3586667 9.3837 9.9110333 9.1884333 8.9687 8.1091333 11.083 9.4324333 9.0414 8.4679 8.3565 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6.53472 6.4617 6.33948 6.4413 6.3892 6.4035 6.1788 6.3293 6.5232 6.2929 6.3429 6.1741 6.6358 6.4251 6.5182 6.3021 6.4043 6.541 6.3567 6.2997 6.4218 6.5486 6.5635 6.3313 6.5474 6.7647 6.6026 6.7093 6.5216 6.6258 6.6628 6.6712 6.5376 6.7264 6.697 6.6464 6.5714 6.653 7.0475 6.5251 6.51 6.5672 6.6009 6.5234 6.5517 6.5189 6.6107 6.4551 6.3506333 6.41 6.4190667 6.5299333 6.3754333 6.3455333 6.4818 6.5093 6.482 6.7203 5.8636 5.7853 6.5588 5.5398 5.1792 7.6855 7.8045 6.0794 7.5767 7.34 6.5685 7.5013667 7.4527 6.7478333 6.6823 6.6613667 7.4688667 7.4160333 7.4891667 7.8095667 7.5956 7.126 6.7805 6.8755 6.6795 6.2238 6.3804 6.417 6.2568 6.7985 6.4887 6.8156 6.4217 6.5266 6.5424 6.5555 6.3822 6.4669 6.3844 6.4036 6.6881 6.5617 6.6128 6.5099 6.2206 6.4848 6.4099 6.5819 6.627 6.3574 6.5672333 6.66745 6.6101333 6.579925 6.57875 6.57425 6.60015 6.5936333 6.6148 6.7612 6.61485 6.6112667 6.6430333 6.5678 6.6046667 6.52135 7.5013667 7.4527 6.7478333 7.4160333 7.4891667 6.6897 6.6613667 7.4688667 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8.9271 9.0411 8.9657 8.9786 8.8727 9.0624 8.8492 8.6868 8.6127 9.1191 8.6741 8.5804 8.689 8.7594 8.2002 8.1313 8.4199 9.2489 9.7535 9.9396 10.197 10.298 9.6119 8.3988 8.357 9.8967 9.1156 8.1556 8.6247 9.8822 9.04598 8.98218 9.82414 9.49612 8.857 9.0435 8.8921 8.9215 8.44 8.5486 8.5565 8.6603 8.5396 8.7175 8.8147 8.5568 8.7542 8.3999 9.1121 8.9046 8.8467 9.0606 8.9764 8.7128 9.7855 8.2294 9.9157 8.494 8.8443 8.7719 9.5468 8.9346 9.0746 9.9157 8.3832 8.7311 9.5886 8.4344 10.163 9.8045 9.5981 8.3817 9.8351 9.9688 9.3727 8.7643 8.9872 9.4007 9.4385 10.0696 7.876 7.9086667 7.9820667 7.949 7.8100667 8.0360333 7.9792 9.7873 9.7831 9.6837 9.4177 9.6075 9.7049 9.0131 8.9273 8.6706 9.1306 10.156 8.5298 9.5219667 9.195 9.1585333 9.6913667 9.0832667 8.5550333 9.1381 8.5071667 9.2695333 9.1001667 10.071 9.6473 9.6596 10.569 9.8396 10.689 10.205 10.603 9.8397 9.4263 9.6268 8.3484 8.6211 8.496 8.4602 8.645 8.6256 10.049 10.319 10.089 10.41 11.161 9.9148 9.2661 9.1656 9.5229 9.5505 9.5285 9.7113 8.6861667 8.77675 9.1013 8.420825 8.7407 8.85525 9.1213 8.4581667 8.8874 8.8239 9.41245 8.3763667 8.8108333 8.8092333 9.1164333 8.564375 9.5219667 9.195 9.1585333 9.1381 8.5071667 9.74182 9.0832667 8.5550333 9.2695333 9.1001667 10.019 9.8758 9.3354 10.098 9.8031 9.0268 10.053 9.2039 8.8222 8.9635 8.5399 8.5423 8.8099 8.6992 8.6409 8.8984 8.6709 8.5501 8.9108 9.6072 8.8338 9.0222 8.0289 8.1903 8.0387 9.9537 9.8837 9.6716 8.2859 8.2778 8.013 9.2272 8.086 9.1089 9.3098 8.8851 8.99555 8.85625 9.30075 8.74235 8.86055 8.68675 8.85775 8.52015 9.02215 8.8517 9.3304 8.7345 8.8411 9.2763 9.0038 8.861 9.5601 9.3176 9.3971667 10.563 9.6658 10.08795 9.62465 9.4803333 10.306 9.8215 9.5643333 8.846 10.2652 10.3906667 8.9554333 9.3043 9.8430667 9.6592333 10.083 8.1643 8.1629 9.7819 9.2254 9.0486 8.0651 8.1498 8.8026 8.4245 8.3203 8.3458 8.5574 8.9012 9.7691 8.6609 9.2912 8.9735 9.633 9.3882 8.9902 8.826 8.69 8.1736 8.5598 9.2426 8.4204 9.3403 8.7908 8.8887 8.53 8.6304 8.2113 8.8668 8.5396 8.57235 8.29 8.2453 9.0415 9.0849 9.203 8.8674 7.8006 8.8324 8.2278 9.0639 8.2538 8.9139 +"G1650" 12.18 12.6103333 11.566 10.84 12.0565 12.943 12.765 12.0495 12.3383333 13.124 11.8245 13.058 11.7955 12.529 11.976 12.513 11.5873333 11.8785 11.06 11.97 12.808 12.1953333 12.2265 9.5237 13.135 11.5736667 11.7696 12.474 11.8978 13.5395 13.546 9.8858 12.136 12.5943333 12.558 12.1206667 12.1765 11.406 11.9288 11.936 12.6655 12.489 9.2606333 11.636 10.253 12.364 11.0673333 10.801 12.278 12.013 12.368 11.798 12.33125 9.4736333 12.898 11.9036667 12.022 12.016 10.8993333 12.062 9.9597667 12.364 12.209 11.9765 11.387 11.8396667 12.368 11.312 8.8953 13.296 11.4685 11.795 11.9436667 12.7752 12.2464 12.445 12.192 11.9326 13.173 13.013 12.312 12.436 12.4163333 12.3236667 12.432 12.3696667 12.3253333 12.326 10.596 12.1426667 13.181 12.428 11.505 11.878 11.977 11.6746667 12.086 12.875 12.595 12.963 12.815 12.215 12.334 12.182 12.507 8.8929333 12.498 12.843 12.471 12.37 11.916 12.485 12.509 12.668 12.318 12.363 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9.3540333 8.9939333 8.9191 9.1313 9.441 9.5829 9.0256 8.96045 9.2897667 8.80736 8.83608 9.8487 9.33586 8.90598 10.071 9.6815 9.0987 9.5836 9.1658333 8.9831667 9.0274333 9.1072333 9.0942 9.0473 12.134 8.8799 8.9796 9.213 9.94755 8.8372667 8.7689 8.8193 8.9268 9.4945 9.3818 9.2774 9.9444 9.7808 9.8561 9.5794 9.6529 9.9979 9.4793 9.5523 9.7824 9.609 9.1984 10.179 9.3985 9.3848 9.292 9.3312 9.632 9.3173 9.5892 9.8695 9.3922667 9.4220667 9.0148667 9.8757667 9.1517 8.973875 9.6517 10.106 8.765 9.1467 8.8578 8.8467 8.9352 9.3471 8.88635 9.2125333 9.0068 9.0144333 9.1879333 9.0725667 9.1667 9.2405 9.5885 9.4114 9.04 9.0679 9.1367 9.3538 9.3095 9.6771 9.0591 9.2988 9.2676 9.4367 9.4978 9.6987 9.9315667 9.1102 11.463 9.4707 9.4947 9.1789 9.87535 9.58605 9.7182 9.92545 9.275 9.6134 9.3186333 9.2043333 9.7147333 9.6476 8.9423 8.9682 8.944 9.2075 9.1158 9.1916 9.9837 9.24985 9.0529 9.1296 9.1393 9.0221 9.03915 9.2619 9.70995 9.5741 9.46325 9.4089 8.8475 9.3722 8.858 9.3734 9.4104 9.8823 9.5308 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9.0459 8.5235 8.7236333 9.0373 8.8300667 8.5368 8.5649 8.797875 8.9404 8.8788 8.8631 8.7988 8.7677 8.6013 8.2934 9.0826 8.92205 9.0379 8.7192 8.8301 8.6859667 8.9087333 8.8370667 8.2759667 8.3108 8.7594 8.7287 8.8705 8.8851 9.10535 8.7054 10.438 8.5244 8.3723 8.4444 8.8872 9.0583 8.7956 8.7113667 8.4293 8.1377 9.05245 9.0962 9.1421 8.71135 9.08335 9.34305 9.02395 8.9249333 9.1170667 9.3803333 9.1888333 8.9951 9.3830667 8.6538 8.3822 8.5979 8.6921 8.7954 8.389 9.1348 8.9203 8.76485 8.72505 8.66645 8.5761 8.4123 8.9982 8.7885 8.76445 8.7953 8.85005 8.6727 8.5472 8.2881 8.4781 8.5024 9.1071 8.5869 9.2856 8.4047 8.8173 8.5772 8.5743 9.1728 9.0416 9.219 9.0001 9.2393 9.1293 9.3288 9.2374 9.2862 9.2425 9.2842 9.2727 9.0699 9.1634 9.429 9.2351 9.3369 9.2788 8.791 9.3718 9.2097 9.5399 9.8841 10.57 10.899 11.065 8.7716 9.2231 8.8589 8.7216 9.3881 8.3919 8.8074 9.1595 9.07494 9.1005 8.96764 9.1905 9.3454 9.3515 9.2633 9.4163 9.352 9.3994 9.2842 9.0315 9.3008 9.2329 9.4617 9.2988 9.4766 9.3628 9.5205 9.4902 9.3452 9.2502 9.0755 9.0162 9.2501 9.0046 8.935 9.2019 8.8892 9.0433 8.7749 9.2299 9.0789 8.9209 9.3631 8.9639 9.1452 9.2016 8.6052 8.86 9.0866 9.2016 8.9033 9.0884 9.0723 9.0366 9.0526 8.7385667 8.6326333 9.1074 8.7685667 8.4935333 8.4540333 8.4637333 8.4863333 8.9349 9.7166 10.595 10.783 9.432 9.5469 8.9891 8.4067 8.5926 8.5073 8.9432 8.8004 8.7753 7.7609667 7.9275333 8.3458333 8.5408 8.5902 8.4450333 8.0806333 8.0803 8.1681333 8.0781667 8.6711 8.5902 8.6084 8.6585 9.2312 8.8871 8.9214 9.066 9.1407 9.3214 9.5102 8.9528 9.3423 9.2117 8.9583 8.9342 9.1274 8.9036 8.9601 8.8268 8.8272 8.7037 9.139 9.029 8.9458 8.9963 9.1639 8.9967 9.3223 9.1842 9.21335 9.0185667 8.952 9.1785 9.15295 9.0551 8.9054667 9.20545 9.12185 8.96625 8.949 9.1912333 9.1272667 8.9650667 8.88085 7.7609667 7.9275333 8.3458333 8.0806333 8.0803 8.54484 8.5902 8.4450333 8.1681333 8.0781667 8.5141 8.3879 9.0698 8.6057 8.6269 8.7696 8.576 8.4011 8.9662 9.252 9.1905 9.5163 8.7207 9.0072 9.029 8.6945 9.0205 9.1944 9.546 8.8243 9.2838 9.7224 8.5512 8.3988 8.4825 10.26 10.352 10.35 9.1111 9.1872 9.1078 9.1134 9.1356 10.118 10.017 9.7922 8.85865 9.1632 8.92505 9.27055 8.85835 9.1605 8.9829 9.1343 8.7798 9.01885 8.8982 9.34075 8.7587 8.4309 8.7928 8.7513 8.3735 8.265 8.5499667 8.42705 8.6226 8.84825 8.8715 8.6657 8.51375 8.6118 8.9038 8.8339 8.5964333 8.5729667 8.8994 8.8152667 8.6366 8.6292 9.1906 9.1384 9.2811 8.7577 8.8019 9.2794 9.1482 9.0991 8.8989 8.9996 8.99 8.9894 8.8048 9.0469 8.7167 9.3665 8.6865 9.1773 9.1116 9.1954 8.9693 9.0381 8.8318 9.0177 8.778 9.1133 9.1008 9.0373 9.0689 8.9682 9.2205 8.8352 9.0758 9.1522 9.2551 9.0524 9.07875 9.32385 8.9939 8.9113 8.693 9.938 9.8016 8.9045 10.314 10.205 9.5809 10.407 +"G1734" 8.2744 8.3599 8.48155 9.01945 8.3033 7.9624 8.5855667 7.9317 8.21 8.05455 8.3864 7.9461 8.35695 8.1052 8.5972 8.31405 9.2014 8.39405 7.9742 8.1023 8.2252 8.2784667 8.263 8.4427 7.8545 8.4936 8.27126 8.0965 8.08634 7.71485 8.2885 7.4761 8.0923 7.8862 8.4375 8.3580667 8.4832 7.89615 8.49324 7.9128 8.4709 8.1333 8.8327 8.1097 8.7718667 8.0753 8.4695333 8.1284 7.9784 8.323 7.4251 8.0751 7.8572 8.9059333 7.3213 7.9822 8.3136 7.8605 8.3172333 8.5167 8.7878667 8.0753 8.8395 7.63025 8.2767 8.1512333 7.4251 7.9095 8.0508 8.1716 8.46055 8.64985 8.3146667 8.24566 8.3078 7.9763 8.11858 8.04938 7.83395 8.1687 8.4891 8.15 8.1843 7.9806667 7.8479667 7.981 8.0007333 8.0875667 8.851 8.5389 8.0549 8.7256 7.9399 8.3764 8.4307 8.5673 8.6079 7.8822 8.5909 8.0857 8.7005 8.40335 8.2047 7.6278 8.2105 8.6819667 7.9523 8.3425 8.0002 8.5264 8.3249 8.1258 8.5853 8.2196 8.3734 8.0059 8.2043 8.0668 7.9002 8.5473 8.6234 8.1926 8.4026667 8.3065 8.3507 8.344825 8.2327 8.2194 8.1341 8.3923 8.1973 8.1363 8.0156 8.1557 8.38065 7.9280333 7.9849333 8.0737667 7.9603667 7.8857667 7.9094 8.5527667 8.4261 8.2439333 8.1672 8.4053 8.436 8.2012 7.7381 8.14 8.3158 8.319 8.249 8.2103 7.8291 8.3546 8.0852667 7.7684 8.7878 7.73495 7.89965 8.1143 8.60145 8.2572 8.23885 8.2173 8.1134667 8.1705 8.0654333 8.2082333 8.0723 8.2231333 8.2928 8.1724 7.45585 7.9255 7.8408 8.8626 8.0968 8.04725 8.1866 8.1226 8.14545 8.28 8.51725 7.99915 8.03315 8.35115 8.3502 8.16065 8.264 8.5229 8.7954 8.1783 8.8161 8.0208 8.8392 8.0989 8.1971 8.317 8.3849 8.6828 8.0182 8.0837 8.1285 8.1008 8.0592 8.1328 8.3039 8.4864 7.9617 8.109 8.2754 8.3064 8.0024 8.1406 8.1679 8.4235 7.9383 7.9708 8.2916 8.2059 8.0659 8.1993 8.1146 7.2663 7.2704 6.5679 8.3339 7.9518 8.0524 8.2503 8.0509 8.152 8.1077 8.13518 8.05852 8.06102 8.12504 8.2726 8.072 8.067 7.893 8.1849 8.1918 8.1389 7.854 8.4007 8.2423 8.2744 8.036 8.0327 8.0431 8.0109 7.9688 8.1971 8.2282 8.0561 7.8172 7.8588 7.9959 7.9181 8.0378 7.9147 7.894 7.9743 8.2505 7.9382 7.6764 8.0759 8.0021 7.7396 7.777 7.9087 8.1366 7.8289 7.8925 8.0173 8.1453 7.8838 7.7281 7.9467 8.0732 8.1024 8.2895333 8.3134333 8.5212333 8.4172667 8.4129 8.4251333 8.3553333 7.5786 7.5935 7.189 7.1774 7.0357 6.4967 6.609 8.1052 7.9128 7.3306 8.2766 7.629 7.1263 8.2540667 8.4335667 8.3895667 8.6354 8.6609 8.8958333 8.7714333 8.5739333 9.0643 8.9385 8.9168 8.9988 8.9847 8.2958 7.8017 7.9807 7.9366 7.8722 8.1832 7.9627 8.0483 7.9689 7.8906 7.8674 7.9228 7.8912 7.9378 7.8826 7.9333 7.7028 7.9781 7.7929 8.0488 7.9205 8.1033 8.0237 8.2128 8.2144 8.0261 8.0173 8.0168 7.9338333 7.989275 8.1386 8.16655 8.1174 8.2064 8.2788 8.27265 8.11495 8.1333333 8.1597333 8.0846333 8.1589 7.9802 8.2540667 8.4335667 8.3895667 8.7714333 8.5739333 8.6635 8.6609 8.8958333 9.0643 8.9385 7.7879 7.6661 8.0829 7.7343 7.6631 8.1793 7.9227 7.9722 8.0366 8.2008 8.5104 8.3464 7.9325 7.8158 8.0055 7.9415 8.2398 7.5165 8.0598 8.3883 8.1659 7.8502 8.3863 8.5697 8.3589 7.5569 7.4467 7.7354 7.7879 7.8527 7.7302 7.9266 7.8963 7.6871 7.5085 7.5521 7.5273 7.7561 7.6698 7.65595 7.6389 7.7077 7.5707 7.6447 7.6465 7.6927 7.5066 7.49435 7.33 7.3213 7.4251 7.6022 8.1937 8.1867 8.6831667 8.67495 8.88305 9.04475 9.09345 8.4415333 8.795 9.2666 8.2729333 8.3962667 8.3459333 8.3913 8.1319667 8.0095 7.9984333 8.2191333 7.9942 7.9884 7.968 8.1024 8.1071 7.9593 7.9174 8.0793 8.2017 8.3841 8.1389 8.0095 7.9747 8.2481 8.3369 7.7833 8.0402 7.8676 8.0318 8.1409 7.9667 8.1056 8.0755 8.5174 8.7649 8.1475 8.0453 8.2165 8.2144 8.2293 8.2085 8.2332 8.1348 8.1707 8.22535 8.0464 8.31005 8.3637 8.2852 8.1947 8.2132 8.4613 8.034 8.3188 8.0598 7.7685 8.1769 8.1251 +"G1735" 8.24455 7.8862667 8.01185 8.0884 8.18725 8.3873 7.5947667 8.2042 7.7278667 8.2233 7.93945 8.837 8.17355 7.8957 8.2806 7.84545 7.7785333 8.199 8.5791 8.20075 8.3999 8.1441 8.30785 8.3123 8.186 8.1488333 8.24812 8.2452 8.06918 8.2442 8.4546 7.8461 8.1706 8.2446333 8.543 7.7106667 8.5405 8.16465 8.1944 7.8199 8.02965 8.0398 7.8817 8.2179 8.6514333 8.1408 8.3338 8.0848 8.0841 7.9833 8.0697 7.9812 8.32515 7.6093 8.4392 8.1201 8.08715 7.7504 8.0932667 8.0668 8.0977667 8.1408 8.5493 7.87655 8.1776 8.3559333 8.0697 8.1476 7.9411 8.0659 8.17715 7.97275 8.1187667 8.10398 8.02584 8.1123 8.03318 7.82032 7.8965 8.106 7.9125 8.2881 8.083 8.2369333 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7.0660333 6.6374 6.7922 7.23085 6.65385 7.2434333 7.02235 7.4314 7.6064333 7.1225333 6.9296667 7.0945 7.4365 7.3226667 6.8367333 7.0552333 7.4126 6.7577 6.866 6.971 7.4481 7.0711 6.7455 6.3867 6.7304 7.1652 7.0125 7.0477 7.0411 6.8871 6.3423 7.3457 7.2701 7.1101 7.5398 7.4562 6.8374 6.942 6.8602 7.3496 7.2641 7.029 7.1483 7.2691 6.9816 7.0214 7.3756 7.6657 7.494 6.8953 7.66335 7.1381 7.12065 7.19405 7.4645 8.2978 7.8599 8.9778 10.115 7.2622 7.7031 9.2183 7.8415 8.118 +"G1743" 8.2613 8.2006333 8.05915 9.1841 8.8627 7.9579 8.3314667 8.17915 8.0146333 8.0927 8.17445 9.0025 9.25305 8.9583 8.2222 8.2944 8.3285 9.0849 8.4446 8.59735 8.2939 7.9766333 10.333 8.5432 9.506 9.0125333 8.27528 9.5659 8.57724 7.78915 9.6498 8.2044 8.19 8.8391667 8.5122 8.2320333 9.0865 10.428 8.36994 8.7557 9.3581 9.5506 9.1634667 9.5694 9.3971667 9.3216 8.4875 9.3979 9.1771 8.4558667 9.0715 8.3395 8.4264 8.5207333 9.4602 9.2276333 8.22305 11.643 8.7029667 10.398 8.3214333 9.3216 10.349 8.3422 12.2776667 9.0034333 9.0715 9.2743 9.0271 9.8787 8.2972 8.1445 9.4134667 7.95504 7.97884 11.273 9.05056 8.9462 8.53825 9.8102 8.7245 8.2717 8.7023 9.2496667 8.7657333 9.0417667 8.8286333 8.7773333 9.8344 8.2544 9.104 9.1404 10.3375 8.7168667 8.5121 8.2513333 8.5586 8.9454 9.1992 9.639 9.828 9.49005 9.4801 8.3879 8.6283 9.2455667 11.036 9.8066 10.476 9.3557 10.36 9.6111 9.5809 8.9302 8.9886 7.9794 8.6966 8.0624 8.9499 10.025 11.26 11.1343333 8.5741 9.7269667 9.2754 8.37405 9.1466 10.144 8.4271 8.4102 8.2241 8.4191 9.2195 8.5076 8.2006 8.3019 10.0609667 8.6147 11.0013333 8.4235333 8.3943333 8.3433 8.9979 8.7736333 8.3271 8.2829 8.0977 8.33075 10.744 8.195 9.5245 9.012 8.4393 8.7585 9.1637 8.36 7.9570333 7.9876 10.223 8.3024 8.565 8.751 9.27015 9.0504 9.1197 9.0527 9.332 10.065 8.3629333 9.1747333 8.3485333 8.4804 8.5836 9.2282 8.54935 9.126 9.3739 9.4382 8.1709 8.67785 9.3243 9.16515 9.23265 9.4668 9.2413 8.37805 8.727 9.17925 8.98785 8.7718 11.33 9.23 8.8753 9.0777 9.3709 8.2406 8.5367 8.2528 9.6408 9.0322 9.1305 9.0168 8.595 8.856 8.7859 8.9358 8.4579 8.6834 8.6989 8.7689 8.6629 9.4126 9.2018 9.3026 8.4793 9.2301 8.8535 8.69 8.2595 8.3332 9.9649 9.7141 8.5739 8.3624 8.2011 8.0111 7.7951 7.6416 8.1862 9.5989 12 9.1806 9.2775 8.9291 11.839 8.0999 8.08184 8.34272 8.639 7.9091 7.8892 7.811 8.1501 8.0239 7.9174 8.2412 8.3221 8.8787 8.0375 8.0422 7.7887 8.0471 8.1453 8.3239 8.3942 8.4041 8.521 8.1362 9.9397 8.4045 9.5921 8.3918 9.8918 8.2866 10.074 8.3103 9.9573 7.9517 8.3939 9.8453 8.2471 8.552 9.6101 10.643 9.1633 8.5406 9.496 10.1 8.9787 8.5091 9.1658 9.2132 9.1956 9.3901 12.2673333 8.0559333 8.0896667 7.8697333 7.9437333 7.9450667 8.8875333 8.2793 8.7571 8.0035 7.729 8.7918 7.5809 7.1413 8.9583 8.7557 8.6066 8.7874 9.2955 8.6558 8.6744333 8.8294667 8.944 8.7143667 8.5338333 8.7868333 8.7961667 9.0417667 8.9631667 9.1528333 11.017 8.9152 8.568 11.627 8.5287 8.4374 8.1395 9.9288 8.5107 8.2514 7.9871 9.7621 8.119 8.3775 9.3589 9.6674 8.855 9.3883 9.3057 9.039 9.4341 9.8402 8.095 7.8364 8.983 8.0309 8.1034 8.2681 8.2844 8.1385667 8.09505 8.225 8.193675 8.05345 7.9576 8.24755 8.2277667 7.93065 8.13725 8.39605 8.1569 7.9486667 7.9623667 8.2291333 8.1927 8.6744333 8.8294667 8.944 8.7961667 9.0417667 8.70728 8.5338333 8.7868333 8.9631667 9.1528333 9.8277 8.8352 8.1986 9.5282 9.2792 8.042 9.7836 9.1087 8.2308 8.3178 8.2297 8.4188 8.1556 9.0916 10.002 9.3514 8.9942 10.214 9.3209 8.88 8.9135 7.985 8.6124 8.3981 8.3024 8.6164 8.1709 7.954 8.4282 8.3071 8.4065 8.4002 8.3303 9.1169 9.3502 9.5894 8.5807 7.97405 8.47865 8.13705 8.3353 7.942 8.4288 8.47505 8.49995 8.0614 8.5937 7.98235 8.7594 9.4602 9.0715 9.3196 9.4259 8.5538 8.4226 8.93565 9.89625 8.8178 8.5861 8.3787 10.1654 8.5959 7.7210667 9.4284667 9.0156 9.2786333 7.8283 8.3811 9.5879 8.6122333 8.5599 9.2232 9.5886 10.561 9.64 8.5245 9.6813 10.066 10.99 9.8584 9.0412 9.3723 9.6123 11.194 9.6076 8.4105 8.9962 9.5994 9.8092 9.9911 9.2615 9.3528 9.4406 9.1446 9.4121 8.4939 8.6369 9.2747 9.0618 8.9035 8.1324 8.2338 8.4868 8.7512 8.2119 8.18595 8.3765 8.3431 8.4736 9.3989 8.9522 9.058 8.3324 8.3247 9.795 7.7431 9.9292 8.0129 +"G1744" 8.656 11.6783333 8.1933 9.6889 8.90295 6.6914 12.0833333 8.4833 11.872 8.7519 7.99095 9.5962 9.14395 6.0721 7.4113 8.2459 12.997 8.69855 10.252 8.4227 9.42965 11.1206667 8.67955 6.8728 8.7615 8.1791333 8.38732 9.1361 7.68816 10.0315 10.013 7.8653 8.7062 8.4506 8.8224 7.4383 8.0485 9.2644 8.74468 6.0144 8.5144 8.8798 6.6443333 9.1388 8.6481 8.008 8.6983667 8.2203 8.0565 8.4698 5.9589 8.0814 8.701625 6.7608333 6.045 8.2397667 8.05855 7.1896 9.8768333 8.1995 9.0783333 8.008 9.4357 8.27615 7.6757 7.8457333 5.9589 7.6599 7.226 9.8909 9.2152 9.8093 8.5107333 8.93558 8.84522 10.03 7.27982 7.25234 7.60705 10.18 7.756 8.5553 8.0396333 9.4711667 9.1573667 8.8916 8.6586 8.8333667 9.4945 7.8645 6.8433 7.4146 8.99735 8.6807 9.0393 8.6902 9.1177 7.2017 8.3738 8.5504 8.9263 8.45845 8.5489 8.6139 8.1859 6.7256 7.7545 8.1722 9.0716 8.0705 8.7052 9.1526 7.9599 7.9458 8.3647 8.8173 8.9648 8.6864 8.748 8.9979 8.1210333 8.0797667 9.2717333 8.4492333 8.0942 8.53395 8.6148 8.3312 9.4378 7.864 9.0763 10.447 6.6932 8.0913 8.2026 8.9017333 7.6906 8.0003333 8.4592333 8.6475 8.3665 7.0964333 6.3239 8.1384 8.6792 8.4419 7.6958 8.1954 7.3853 8.9526 9.8164 9.4326 10.409 8.6821 7.6754 8.7378 9.8643333 7.6912 10.117 8.0708 8.50825 8.62225 10.5135 9.39945 10.3505 10.4065 7.679 7.3881333 7.4262667 7.8929 8.2343 8.0363333 7.6648 7.1586 7.97205 7.4416 7.1903 12.537 11.049 8.0832 7.4106 7.721 7.96515 7.92015 8.796 7.94765 7.75265 8.02115 8.1307 8.2493 7.3962 7.4702 7.8051 7.7219 12.554 9.5469 12.52 11.339 7.3554 7.6037 7.8762 7.5774 9.0333 9.7898 9.1888 9.5143 9.0418 9.1857 9.6917 10.167 9.1805 9.1239 9.754 10.996 9.0986 9.5088 9.9758 10.887 9.1326 8.9142 9.0823 9.0814 8.6928 8.4562 8.3293 7.6524 7.0407 6.5511 10.46 10.357 11.051 10.673 10.828 10.668 10.626 7.6527 7.73072 7.51358 7.55616 9.853 9.619 10.112 9.0213 9.1416 9.9811 10.119 10.322 10.963 10 10.062 9.7289 9.5904 10.02 9.777 10.252 10.614 10.771 8.9592 8.9608 8.4623 8.6989 8.9809 9.3401 9.1926 9.8669 8.943 9.3106 8.3974 8.5637 8.6613 8.8772 9.0113 9.1284 9.2189 9.1135 9.0375 9.1799 9.4949 9.645 8.8914 9.3094 9.5394 9.2714 9.23 10.1855667 9.3661667 9.4731 9.3811667 9.8843667 10.2316667 9.9581667 9.4857 8.4201 7.7495 6.2507 7.135 5.9877 5.4617 6.0721 6.0144 7.6839 8.5464 6.35 7.368 6.5051667 6.7134333 9.2368 8.8349333 9.2602667 8.1838 7.1035333 6.9026333 6.7495333 7.1922667 12.28 12.235 12.571 10.812 11.167 8.7951 9.7874 10.553 9.8201 9.8465 9.0545 8.9493 9.3374 8.9197 9.0653 9.1019 9.5044 9.0935 9.1025 8.1165 8.9719 9.0128 8.6146 9.3552 9.9076 8.8429 9.1365 8.8566 9.2266 8.1345667 8.1205 7.8101333 8.220275 8.12655 8.2603 7.5777 7.9616667 8.1008 7.9619 7.7272 7.8751667 8.3041 8.3063333 8.1036 8.40775 6.5051667 6.7134333 9.2368 7.1035333 6.9026333 8.77784 9.2602667 8.1838 6.7495333 7.1922667 9.6003 9.8954 8.4138 8.3896 7.9331 9.6113 10.18 9.3321 8.679 8.7177 9.9601 9.3185 9.3977 8.6621 8.7699 9.6344 8.3396 8.5123 8.3351 8.1571 7.5067 7.9583 8.9367 9.0644 9.3121 7.5402 7.8913 8.034 8.4538 8.3451 8.8076 8.3673 8.9369 8.1505 8.5449 8.2191 9.06315 10.8885 9.2786 10.694 9.2708 10.781 9.44825 10.882 9.1932 10.8525 9.43025 10.868 8.3739 6.045 5.9589 7.2457 6.1323 6.9172 8.9797333 8.9974 9.11305 9.8228 9.47985 9.0316333 10.193 10.71 10.0388 9.6742667 9.5942667 9.4783667 9.3061 8.7642 8.4591 8.7305 8.2611 8.2568 8.1559 7.8888 8.2152 8.4377 8.7642 8.7437 9.0402 9.419 8.0392 8.192 8.0468 8.377 8.1691 8.6329 8.7389 9.3106 8.8997 9.2752 8.1653 8.1771 8.185 8.7129 8.7171 8.1212 8.3861 7.8184 8.1433 8.1929 9.0796 8.612 8.9712 9.081 9.1111 9.2331 9.8093 9.66255 10.217 10.651 11.687 10.69 11.725 10.224 10.839 10.075 10.415 10.074 +"G1745" 9.12385 9.0796333 9.15785 8.6337 8.9866 8.0117 9.5301 9.46995 9.0308333 9.72235 9.3792 8.5596 8.60735 8.4637 8.4501 9.2281 8.5103667 8.9661 8.5063 9.6624 8.2992 8.2646667 8.39415 8.2138 8.7287 9.2295667 9.46326 9.8339 8.77532 9.0681 8.6204 8.6707 9.5558 8.9269 8.4784 9.204 8.56185 8.4789 9.06142 8.1139 8.4246 8.8618 7.8895667 9.1301 8.4101333 8.3312 9.0177333 8.2854 8.5073 9.0893667 8.5799 8.9597 8.80015 7.9729333 7.8728 8.935 8.8537 8.5415 9.2381333 8.9186 8.9597667 8.3312 8.7643 8.5025 9.5039 9.0136333 8.5799 8.8552 8.0388 8.8968 8.995 9.06635 9.0141333 9.36812 9.57754 9.2848 8.38622 8.55348 8.96395 8.6561 7.8532 9.2819 8.7873 8.9938667 8.8344 9.0824 8.7564333 9.0085333 9.3528 9.6529333 7.892 7.848 8.7817 8.6554667 8.8338 8.7353333 8.8643 8.7837 8.8419 8.648 8.3175 8.95975 8.8428 9.2264 9.1806 7.9396667 8.4983 8.8202 9.0766 8.7894 9.8117 9.1317 8.7573 8.9714 9.1705 9.5231 9.0288 9.1387 8.829 9.4881 8.916 8.9932 9.1908333 8.5502667 8.9558 8.9283 9.5586 9.3956 9.0268 9.3084 9.0333 8.998 8.1006 8.8917 9.1981 9.1376 8.5629333 8.5204333 8.4977667 9.0053667 8.8662667 8.0403667 7.697 8.5968 9.0882 8.7287 8.7373 9.39505 8.3012 10.922 8.3969 8.1078 8.3246 8.9996 8.6134 9.4177 8.7081333 8.6223 8.8803 8.976 8.89395 8.95755 8.90425 9.2431 10.158 9.9584 9.2388333 9.1877 9.2269667 9.4488667 9.3257667 9.6821 8.7781 8.1116 8.5855 8.8828 8.5941 8.9726 8.6809 8.9892 8.4128 8.66 8.75665 8.64235 9.01535 8.17595 8.16065 8.2777 8.24905 8.4944 8.1626 8.1111 8.5538 8.75 8.6363 8.5468 8.832 8.8182 8.1269 8.326 8.858 9.0207 10.068 10.069 10.176 10.167 10.246 10.476 10.678 10.915 10.193 10.537 10.596 10.479 10.051 10.218 10.327 10.037 10.138 9.5311 9.4322 9.2156 9.4273 9.417 9.3453 10.264 9.3092 9.1844 9.1227 8.8455 8.4663 8.5249 9.0513 8.3843 8.4256 10.11514 9.37536 9.42334 9.47712 10.17 10.147 10.223 9.7774 9.7031 9.1648 8.5763 8.2282 8.8635 10.187 9.7334 9.5162 9.0287 9.2964 8.2801 8.3464 8.146 8.5118 10.176 9.6126 9.1083 10.135 9.5847 10.16 10.678 10.481 9.8459 10.327 10.306 9.2903 9.6162 10.289 9.0358 9.4207 9.2502 8.731 9.054 9.4187 9.7168 8.4659 9.9313 10.163 10.186 10.024 9.0894 8.7223333 9.1185 10.6756667 10.5643333 10.383 10.2236667 10.2153333 9.5014 10.111 10.09 9.8057 9.6385 9.145 8.6085 8.4637 8.1139 8.9647 9.0329 8.0749 8.3086 7.8715333 7.8275333 8.0641667 8.2693 8.1698667 8.3774 7.7623667 7.7169 8.0797333 8.0196667 8.8269 9.14 9.0593 8.7815 9.1318 8.7197 9.119 9.7447 9.7943 9.8993 10.111 8.9629 9.7519 9.4425 8.7965 9.3924 8.8852 8.9331 8.7773 8.797 8.5397 8.6621 9.065 9.4858 9.6555 9.5483 9.6753 9.676 9.6398 10.204 10.208 9.6666667 9.782225 10.2635 10.29 9.3778 9.8036667 10.0898 10.212 9.5194 9.8418333 10.2916667 10.2773333 9.6066667 9.840325 7.8715333 7.8275333 8.0641667 7.7623667 7.7169 8.26568 8.1698667 8.3774 8.0797333 8.0196667 8.6437 8.7436 9.0257 8.2705 8.645 8.5773 8.4958 8.4641 9.8828 10.515 10.636 10.647 9.0082 9.0851 9.54 8.8424 9.0245 9.0357 9.1676 8.8672 9.4122 8.9506 10.341 10.168 10.111 10.207 9.8595 9.4573 9.2678 9.3518 9.4154 9.2091 9.6719 11.41 10.584 10.897 9.233 10.1805 8.4387 9.83215 9.51235 9.95825 8.66975 9.67535 9.17285 9.92945 8.5885 10.1735 8.481 7.8728 8.5799 7.9158 7.9197 7.8835 8.7767 8.50725 8.7123 9.1389 8.88835 9.0528667 8.76815 9.1676 9.3616333 9.2658667 8.6854 8.3722 10.3446667 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7.8765667 8.0511667 7.8732 7.78955 7.4741 7.6198 7.401 7.275 7.3747 7.525 8.0739 7.2073 10.7735 7.4102 7.4371 7.5998 7.5829333 7.3543333 7.4532 7.7842667 8.3666 8.0921667 7.8486 8.0245 7.7822 7.56225 7.8985 6.6637 7.5599 8.0753 8.0721 7.7037 7.7668 7.9011 7.7124 8.0898 8.0095 10.569 9.74005 9.67885 7.67585 7.42345 7.3726 7.3763 7.8013 7.4836333 7.6362 7.3993 7.442 7.5057 8.0589 8.424 8.38325 8.2525 7.9839 7.8828 8.2322 7.85035 7.9807 7.8336 7.94615 7.9968 7.9146 8.4096 8.38385 7.8945 8.13585 8.14695 7.9048 7.415 7.9978 7.896 8.1391 8.2073 8.2538 8.2432 8.1072 7.8941 8.1454 7.7604 7.3025 7.3436 7.2632 7.3701 7.4335 7.3408 7.393 7.446 7.4732 7.0938 7.4035 7.2432 7.344 7.354 7.5173 7.5482 7.1863 7.4147 7.1547 7.6294 7.4371 7.4821 7.4025 7.2824 7.4041 7.2432 7.7476 7.5971 7.9858 7.535 7.2991 8.023 8.0382 7.5475 7.54626 7.50864 7.51138 7.3967 7.3357 7.3027 7.3282 6.8888 7.1878 7.373 7.6191 7.6255 7.1612 7.3817 7.1925 7.3008 7.2953 7.585 7.3567 7.6334 7.6507 7.2895 7.6433 7.6672 7.7535 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7.1758 7.3187 7.0407 7.5113 7.5015 7.6897 7.6053 7.6362 7.5242 7.4849 7.4151 7.4401 7.55555 7.0425 8.0555 7.26895 7.5858 7.27755 7.81875 7.05815 7.72935 7.1657 8.1517 7.00765 7.7357 8.8037 8.0303 7.7236 8.5135 8.3389 7.7921667 7.88565 8.06115 8.8268 8.9799 7.8330333 9.0602 8.9711 7.4755667 7.5589333 8.1331333 8.3347 7.1815 7.5952667 8.1429 8.1396667 7.5295 7.4112 7.7727 7.9931 7.8605 7.2742 7.4805 7.5651 7.7954 7.4003 7.7168 7.7659 8.0044 7.9103 8.1057 7.0587 10.915 10.909 10.873 10.618 7.4398 7.6621 7.5733 7.7409 7.5173 7.356 9.5766 11.375 11.042 11.038 7.2275 9.7664 7.9036 8.1326 7.17575 9.86135 7.85025 7.50875 7.6091 8.9763 8.0935 7.5875 7.2429 7.6203 7.2471 7.5039 7.6589 7.4866 +"G1784" 10.32 10.2526667 10.644 8.16545 9.79105 9.3396 10.5173333 11.091 10.3726667 10.659 10.5025 8.6692 9.34275 8.943 9.2274 10.6995 7.1662 9.94385 8.0757 10.673 8.95075 9.3700333 9.5746 8.4238 10.052 9.9395333 10.9982 11.036 9.96188 11.254 9.4985 8.2024 10.939 10.643 10.1 11.1516667 8.8676 9.3072 10.1584 8.4494 9.07375 10.084 8.8326333 10.307 7.7842 8.9671 10.433 9.5387 9.884 10.1866667 9.0007 10.00865 10.45225 9.1438 9.0873 10.1836667 9.8453 9.4741 10.7753333 9.1964 8.1725667 8.9671 9.017 9.4265 9.4288667 9.9224 9.0007 9.4705 8.4844 9.9114 9.9573 10.169 9.845 10.9874 10.8652 10.196 9.4026 10.01856 9.72265 9.6794 8.8268 10.53 10.3063333 10.5046667 10.348 10.5013333 10.479 10.5046667 9.6726 11.1483333 8.7162 8.7648 9.4265 9.8776667 10.407 10.1666667 10.483 8.7746 8.9909 9.0426 8.772 10.03755 10.171 10.608 10.489 8.7961667 9.9977 9.8126 10.152 9.3315 11.075 9.8048 9.6054 9.9089 9.2775 10.92 9.8415 10.418 10.037 10.276 8.7851 8.8565333 10.2226667 8.9809 8.4794 10.15175 10.159 10.337 10.431 10.294 10.174 10.251 8.5224 9.9398 10.8445 10.667 10.1005667 10.4233333 9.9766333 10.543 10.511 9.8001333 8.6108 9.2639 10.184 9.832 10.191 10.549 9.5825 11.951 9.1664 8.7363 9.4316 9.4841 9.515 10.273 9.3007 8.5386 8.4569 9.6259 9.27555 9.7347 9.16435 9.3339 9.7896 9.87565 9.8596 9.5476333 10.0659667 10.473 10.6003333 10.3166667 8.7102 8.8327 8.56635 9.7428 9.6864 9.1191 10.032 10.4925 9.3846 9.5494 9.2797 9.44295 8.19775 9.8414 8.99995 8.44425 8.53165 9.06535 9.5367 8.9918 8.0868 8.5989 8.7548 9.5756 8.349 9.9313 8.9815 9.08 8.5909 8.7415 10.786 10.891 10.725 10.885 10.868 10.851 10.655 10.184 10.987 10.632 10.402 10.138 10.808 10.403 10.491 10.201 11.378 10.912 10.409 9.7724 10.495 10.239 10.385 11.637 11.737 12.036 10.527 10.155 9.1772 9.1094 10.353 9.2935 9.178 11.0326 10.20654 10.10764 10.2728 11.426 11.581 11.416 11.333 10.749 9.3481 9.3202 9.0944 9.601 11.326 11.202 10.909 10.021 10.106 9.4836 9.3161 9.1818 8.6225 11.613 10.806 10.771 11.051 10.521 10.29 11.421 10.69 10.766 10.733 11.735 10.728 10.867 11.544 10.416 10.372 9.7436 9.3187 10.295 10.367 9.6002 9.2226 10.799 10.827 10.786 10.667 10.447 9.6821667 10.5353333 10.7906667 10.6523333 10.6226667 10.585 10.5746667 11.178 11.19 11.598 11.998 10.671 10.615 10.452 8.943 8.4494 7.9157 9.0818 8.5366 8.5699 8.7399333 8.8206667 9.4337 9.0107 9.3861333 8.8310333 8.9170667 8.7793333 8.6414 8.4801333 8.2724 8.3185 8.4957 10.382 10.646 10.891 10.134 11.174 11.368 11.262 11.29 10.543 11.324 10.966 9.7844 10.693 9.5026 9.9571 9.8702 9.0117 9.3735 9.213 10.587 10.636 10.785 10.475 10.344 10.262 10.45 11.3786667 11.3395 10.5704333 10.2711 11.2945 11.3425 10.2655 10.3933333 11.3415 11.221 9.88245 10.4823333 11.5036667 11.3746667 10.3311 10.367 8.7399333 8.8206667 9.4337 8.9170667 8.7793333 8.9325 9.3861333 8.8310333 8.6414 8.4801333 10.171 10.381 10.265 9.2674 9.5887 9.8489 8.8731 8.7916 10.803 10.98 10.768 10.5 10.678 10.655 10.582 9.2548 10.341 10.249 10.749 9.9563 10.88 11.023 10.313 9.7085 9.8454 11.627 11.382 11.148 10.399 10.577 10.376 10.514 10.984 11.702 10.888 10.738 11.148 11.6285 10.1285 11.315 11.2345 11.3475 10.274 11.075 10.7855 11.3365 10.109 11.5335 8.8893 9.0873 9.0007 9.3424 9.3212 8.4612 10.1223333 8.3076 8.56275 8.13005 8.26895 10.475 8.27415 7.9175 11.051 10.785 9.6671 9.1229667 11.8163333 10.7066667 9.5304 9.4142667 10.663 10.476 9.9376 9.0865 9.1775 10.973 10.656 10.253 9.308 10.063 10.571 10.652 10.1 9.6447 9.3505 11.4 10.613 10.894 10.062 10.313 10.637 10.815 10.694 10.288 9.7168 10.944 11.169 10.443 10.749 10.506 11.424 10.791 10.818 9.7848 11.4285 10.9655 11.0605 10.2285 10.296 9.7959 9.6054 10.714 10.908 10.249 11.711 11.98 11.562 11.701 +"G1785" 8.70695 9.0233333 8.4524 7.80685 8.56865 8.2095 8.9475 8.65465 8.9232333 8.76655 8.5654 9.0641 8.4959 7.2313 8.2716 8.8218 9.0431333 8.65275 7.4705 8.78365 7.97705 8.5100667 8.5794 7.3383 8.6626 8.3382333 8.64884 9.1357 8.42912 8.3413 8.6851 8.3012 8.9701 9.2483667 8.5516 7.8436333 8.12255 7.03485 8.5671 7.8235 7.8257 7.953 7.6907333 8.1364 8.5169 8.2015 8.6867 8.5156 9.0852 8.5402 7.7609 8.37995 9.0179 7.4966667 8.3331 8.8380333 7.84005 7.6702 9.5331667 7.7805 7.7258667 8.2015 7.9421 7.59485 7.9941667 8.3457 7.7609 8.1676 7.9693 8.8826 8.47965 8.64665 8.4834 8.64758 8.86578 8.696 8.21802 8.1664 7.0606 9.1277 7.6376 8.5094 8.9234667 8.8996 8.6621 9.0346667 8.9446 8.7895333 7.7395 8.1958333 8.1737 8.0254 7.58985 8.4571333 8.8169 8.1261667 8.7678 7.4734 8.0244 8.2213 8.5274 8.4683 8.0304 8.1707 8.2361 7.7760333 8.2892 7.6252 8.408 8.1171 9.3079 8.2348 8.2717 7.9008 7.771 8.8218 8.7693 8.737 8.3796 8.9989 7.8807 7.8586 8.4366333 8.1853 7.7914 8.73505 9.0469 8.4461 8.738 8.8018 8.9856 9.1485 7.3321 8.1492 8.68625 9.0696333 8.4957667 8.7664667 8.3666 9.0252 8.7742 8.0833333 7.7701 8.4000667 8.3519 7.8662 8.487 8.3783 7.6267 9.1238 8.5514 8.2548 8.7218 7.7605 7.57 8.2353 8.2956333 7.8501 7.6367 7.2578 6.7983 7.3601 7.5992 7.9505 8.1068 8.14725 7.9928 7.9932667 8.0605333 8.3412333 8.5290333 8.3374667 8.1461 7.6236 8.1209 8.0237 8.0378 7.9488 8.9877 8.53365 8.356 8.31435 8.50915 8.4865 8.1434 8.463 8.54765 8.5557 8.43675 8.4906 8.2558 7.6085 7.8481 7.9157 7.5052 8.3171 7.5973 9.0506 8.0656 7.9736 7.8112 7.8965 8.4693 8.6979 8.4185 8.6135 8.6944 8.5399 8.3129 8.2147 8.7991 8.5076 8.6325 8.2202 8.5289 8.5786 8.5476 8.4429 8.7056 8.3463 8.1708 8.9271 8.7057 8.8692 8.5253 8.9671 9.0854 9.1109 8.5514 8.2995 8.0304 7.9492 8.0464 8.266 8.0274 8.58036 8.42768 8.36866 8.07608 8.9666 8.856 8.667 8.892 8.5716 8.1101 8.6977 8.3441 8.4193 8.7355 8.4552 8.5521 8.3481 8.8125 8.4838 8.2167 8.1371 8.3309 8.6013 8.5459 8.3897 8.7854 8.1714 7.9576 8.5124 8.2842 8.3029 8.2345 8.9516 8.3278 8.6006 8.6113 8.3552 8.4627 8.7137 8.1527 8.3952 8.4213 8.2596 7.8396 8.2727 8.6555 8.3228 8.2728 8.7369 8.4111 8.4537333 7.8995 7.7735 7.7791 7.9162 8.0422333 8.523 9.7548 9.5495 9.5449 9.3056 8.5364 8.2167 7.2313 7.8235 7.8935 7.6911 8.038 7.5535 7.205 7.2511667 7.5579 7.9068667 7.9487 7.2584667 7.6235 7.6174333 7.2012 7.5037 7.986 8.0287 8.2052 8.9035 8.5805 9.0836 8.674 9.4747 9.4693 9.5619 9.2556 8.2275 8.8703 8.5036 8.0274 8.4483 8.2269 8.6072 8.5565 7.8721 8.1989 8.3358 8.7159 9.0546 9.0548 8.8256 9.059 9.0087 9.0046 8.8708 8.687 8.5022333 8.615775 8.67795 8.73205 8.4334 8.4467667 8.78975 8.69615 8.3195 8.5615333 8.6997 8.6684667 8.4481 8.8056 7.205 7.2511667 7.5579 7.6235 7.6174333 7.93346 7.9487 7.2584667 7.2012 7.5037 8.511 8.2805 8.5493 7.9369 7.9001 8.353 8.4378 7.6154 8.5849 8.7418 8.824 8.6202 8.3316 8.2019 7.9881 7.9641 8.0565 8.0215 8.6913 8.2338 8.8488 8.8095 7.4605 7.3262 7.4688 9.1153 9.0753 9.757 8.0188 8.2751 8.2419 8.3804 8.4081 9.4517 9.2021 9.3749 8.77275 9.15585 9.07025 9.1947 8.90355 8.86245 9.13605 8.9561 8.5742 8.9868 9.04295 9.1168 7.1405 8.3331 7.7609 7.3112 7.9725 8.061 8.4594667 7.88605 8.3756 8.7706 8.4352 8.5401667 8.49355 8.4517 8.5084 8.4029 8.5108333 8.6587333 8.6642667 8.5711667 8.1741667 8.0903667 8.4059 8.2062 8.0265 7.7113 8.351 8.4724 8.3044 7.9525 7.7132 8.0363 8.2075 8.3687 8.2412 8.2841 7.9934 8.8764 8.4828 8.8202 8.2119 8.2309 8.3678 8.22 8.3512 8.077 7.8975 8.1548 8.4313 8.2867 8.3238 8.303 8.6088 8.2267 8.5479 8.4167 8.56665 8.3444 8.5701 8.21655 8.7633 8.7743 8.7088 9.3521 9.6436 8.7976 9.9433 10.015 9.7666 9.8329 +"G1786" 7.97235 7.6939 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9.5833 9.3412 8.8171 8.4570667 8.2823667 8.5850667 9.1186333 8.5949333 8.8435333 8.3119 8.3350333 8.8670333 8.676 10.237 9.506 9.3269 9.4429 9.1943 9.3891 9.5872 9.5583 8.904 8.8181 8.9056 10.332 8.8632 9.6108 10.435 10.021 11.038 10.7 10.643 9.4171 9.4446 10.026 9.1549 9.6238 9.3787 9.2175 9.4046 9.2419 9.3842 8.5382667 8.5318 8.9708667 9.270875 8.73765 8.53215 9.0789 9.2437333 8.5177 8.68135 9.207 9.2277333 8.4334 8.4378667 9.0914333 9.107525 8.4570667 8.2823667 8.5850667 8.3119 8.3350333 9.17472 8.5949333 8.8435333 8.8670333 8.676 9.9712 9.2354 9.5149 9.4914 9.1868 8.5975 9.9803 8.8992 8.5809 8.5971 8.5117 8.6198 8.5207 9.0936 9.1902 9.5294 9.1788 9.2358 8.7527 9.267 9.0169 8.4781 8.9208 9.3159 9.061 8.6092 8.6665 8.5274 9.6863 9.3213 9.2874 8.9586 9.2341 8.5741 8.2394 9.0813 8.5444 8.09225 10.0555 8.06575 8.66485 8.0405 9.98455 8.23925 8.71975 8.0282 10.0625 7.96205 10.839 9.741 8.8234 10.593 9.8067 9.7707 8.4489 10.5285 10.105 10.2745 10.4555 8.5620667 10.5885 10.32 8.6776667 9.4774333 10.4816667 10.6386333 8.5615667 9.7563 10.4972667 10.1347 9.097 10.021 10.847 10.672 10.156 8.9059 9.7576 10.574 10.423 9.4041 9.8897 9.7013 10.533 10.767 9.9598 8.4634 9.2269 8.9116 9.4315 9.0889 8.9164 9.2002 9.384 9.0821 9.2542 8.8678 8.3766 9.2192 9.1242 9.3551 8.5639 8.6658 8.8302 8.8727 8.3694 8.7987 8.6583 8.70905 8.4772 8.2797 8.5187 8.5193 7.9773 8.4287 8.5253 7.7222 8.16 8.0747 +"G2234" 9.72195 9.9716333 10.3285 9.3923 10.4095 10.139 9.8211333 10.808 10.252 10.7035 10.345 11.005 10.472 9.3538 10.314 9.81835 8.7864667 10.30845 10.471 10.627 10.1325 9.6981667 10.726 9.5675 10.77 10.4476667 11.3684 10.815 11.1196 10.485 10.763 9.0713 10.721 10.6266667 10.257 10.4046667 10.537 10.1715 10.5714 8.8775 10.2774 10.534 9.3959333 10.606 10.403 11.006 10.483 10.591 10.054 10.91 9.8652 10.088 10.66025 9.7568333 11.341 10.2996667 10.469 11.552 10.9503333 10.532 10.3039 11.006 10.002 10.4835 10.709 11.1663333 9.8652 10.55 9.3084 10.398 10.282 10.0458 10.522 10.812 10.8292 10.29 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10.116 9.7644 10.042 9.862 10.097 10.006 9.8437 10.053 9.9287 9.86 9.473 9.5043 9.2459 9.3408 9.187 9.2001 9.394 9.0716 9.0087 8.6046 9.7668 9.6502 9.7399 9.3561 9.6041 9.3489 9.6904 9.01318 9.13494 9.47886 9.36898 9.9645 9.9846 9.8425 10.031 9.7255 9.5457 9.5884 9.4653 9.5169 9.7904 9.8227 9.6299 10.002 9.6779 9.6097 9.1633 9.212 9.4927 9.6772 9.7432 9.893 9.9476 9.7989 9.7798 9.5764 9.6696 9.5926 9.5615 9.9063 9.9057 9.7528 9.6447 9.681 9.8753 9.8212 9.9228 9.629 9.6104 9.7492 9.7969 9.4237 9.538 9.5845 9.5682 9.7557333 9.8588333 9.5145667 9.4712667 9.2299 9.0803 9.1826667 9.2407667 9.8042 8.5033 8.7563 8.7912 8.7317 8.5307 8.5504 9.5353 8.3367 8.2447 9.5617 8.4177 8.8094 9.3642667 9.6225 9.2287 9.6337333 9.2679333 9.4331667 9.5148 9.4965333 9.7775667 9.6225 9.419 9.517 9.5714 9.2035 8.9345 9.2439 9.1338 9.089 9.0577 9.5366 9.3353 9.7369 9.562 9.7003 9.5999 9.6496 9.384 9.6347 9.8801 9.4614 9.3969 9.6536 9.9769 10.106 10.086 9.7826 9.7196 9.5799 9.7678 9.5050333 9.46835 9.5100333 9.5736 9.4987 9.55965 9.61055 9.5798667 9.4775 9.50565 9.56925 9.5934333 9.5213667 9.5294333 9.576 9.5223 9.3642667 9.6225 9.2287 9.5148 9.4965333 9.57406 9.2679333 9.4331667 9.7775667 9.6225 9.2647 9.1473 8.6668 8.8825 8.7724 8.5107 8.7493 8.7703 9.9112 9.9643 9.8684 9.8676 9.348 9.7207 9.7595 9.2195 9.6956 9.2463 9.6958 9.6259 9.2343 8.9129 9.2035 9.2762 8.7718 9.0498 8.7819 9.189 9.3119 9.5651 9.5044 9.5507 9.3737 9.0737 8.6772 8.9187 9.49625 9.6863 9.4856 9.53875 9.40015 9.58465 9.4553 9.21265 9.61025 9.5069 9.42845 9.6616 9.478 9.2019 8.3287 9.2659 9.2771 9.1262 9.0735667 9.02205 9.22655 9.78345 9.46005 9.1552 9.6102 9.7889 9.5436 9.3700333 9.5846667 9.5052333 9.2184333 9.3450667 9.3441333 9.3420333 10.024 9.6647 10.03 9.3736 8.91 8.1747 9.5321 9.4238 9.0299 8.992 9.7265 9.9767 9.7089 9.5921 9.5752 9.6971 9.6513 9.4809 9.5827 9.6788 9.5686 9.4599 9.3026 9.0837 9.57 9.5883 9.557 9.6093 9.2929 9.4686 9.7087 9.2428 9.5018 9.3491 9.5878 9.60465 9.45265 9.3549 9.1856 8.7823 9.1099 9.0785 8.9732 9.3148 8.6429 8.9906 8.8908 8.9593 +"G2642" 8.6402 8.6476333 7.9118 7.9021 8.8032 7.7244 8.5260667 8.4433 8.7927667 8.71265 8.1215 8.5544 8.4005 7.5691 7.7596 8.42605 8.1550667 8.8363 7.3984 8.4738 8.27965 8.4209667 8.2524 7.6965 8.0129 8.5849 8.32988 8.4049 8.02662 8.76015 7.9793 7.9199 7.9472 8.4733 8.6896 7.6696333 7.71585 8.4749 8.76754 7.8173 7.67455 8.3046 8.0559 8.2694 7.6231667 7.7192 9.2762667 8.4578 8.2059 8.4537333 7.5746 8.9007 8.6209 7.9726 7.8644 8.3128333 8.3346 7.6425 8.2466 8.5248 7.9743333 7.7192 8.3463 7.56005 9.1179 8.1843333 7.5746 8.3759 7.8322 7.688 8.62175 8.4441 8.7088 8.30406 8.1044 8.1946 7.94236 8.0621 7.88155 7.9334 7.7741 8.2891 8.1589667 8.1861333 8.1948 8.3731 8.2124 8.0888 8.213 8.1081667 8.0701 7.7497 8.3721 8.3631333 9.0329 8.1161667 9.0233 7.5166 7.6463 7.8966 8.0377 7.89075 8.4434 8.2145 8.3634 7.9992333 8.2888 8.2844 8.423 8.2188 8.3267 8.7725 8.3542 8.2057 7.8711 7.9342 7.8693 8.1277 8.1788 8.1326 8.3864333 7.7060667 8.8642333 7.8378667 7.4171 8.4863 8.3006 8.3 8.2962 8.3207 8.2546 8.7168 7.5738 8.6618 8.19735 8.5259 8.2438 8.448 8.0442333 8.5316 8.3670667 8.2106667 7.5139 8.1862667 7.9405 8.3332 8.4282 8.0284 7.9319 7.2821 7.9792 8.0713 8.3019 8.338 8.3936 8.336 8.0539333 7.5882 8.2759 7.54055 7.49355 7.51405 7.52405 7.7975 7.68295 7.55725 9.8833 8.7691333 8.3346667 8.1226 8.4023667 8.1825333 7.7372 7.4794 7.53075 8.1992 8.1736 8.8165 8.148 8.1851 7.9444 7.7675 7.72305 7.71315 7.77495 8.1843 8.2898 8.026 7.8793 7.93405 8.1326 7.8384 7.5054 7.6089 8.1073 7.9779 8.2293 8.2101 8.2361 8.0493 7.5393 7.4924 8.12 8.1429 8.5434 8.3354 8.0901 8.2169 7.9678 7.7036 8.0329 8.0738 8.0518 7.8263 8.0356 8.0151 8.2604 8.1127 8.0865 8.2703 8.1415 9.4287 8.1783 7.8622 7.9032 7.7328 7.9624 7.8722 8.2933 8.5626 8.0426 7.9615 8.3282 8.0572 7.9096 7.84606 7.85852 8.18706 7.9047 7.764 7.9379 7.7811 7.8835 8.0497 7.896 8.0808 7.7362 8.3232 7.6711 7.5704 7.9808 8.1108 8.1052 7.8305 7.7318 7.6923 7.7544 8.6131 8.2699 8.1213 9.7058 7.9717 7.6077 7.9605 7.5993 7.9662 7.3919 8.0407 8.2708 8.3968 8.2604 8.0513 8.5133 9.4294 7.8321 7.986 8.2669 8.8096 7.6296 7.7866 7.8253 7.9686 7.916 7.6574667 7.8215333 8.0038 7.4606 7.2663333 7.2898667 7.3959667 7.2538 8.8046 8.2221 8.0652 7.9622 8.9596 8.5199 8.6838 7.5691 7.8173 7.4993 7.5274 7.6093 7.5942 7.5861 7.5807667 7.8561667 8.1248667 8.2054333 7.7846667 7.6686333 7.5816 7.5229333 7.7922667 7.7303 8.1003 7.8637 8.1418 8.4076 7.973 7.6675 7.6924 7.5689 7.975 7.9127 8.3317 7.9326 8.4999 8.251 8.1029 8.0201 7.8359 7.8207 7.4949 7.6583 7.5688 8.2711 8.0357 8.1901 7.7176 7.3879 7.5676 7.5499 7.9561 7.9729 7.9442 7.8084 7.84735 7.88305 8.11425 7.8127 7.8377 7.79375 7.95585 7.7198333 7.7963 7.7964333 8.0327667 7.851675 7.5861 7.5807667 7.8561667 7.6686333 7.5816 8.05144 8.2054333 7.7846667 7.5229333 7.7922667 8.3274 8.5185 8.7138 7.7491 7.9766 8.3897 7.6229 7.6902 8.0413 7.7807 7.5443 7.3742 7.8302 7.6782 8.1991 7.3444 7.7584 7.7685 8.2964 7.879 7.7287 7.6811 7.0028 7.4547 7.1866 7.5999 7.3389 7.5789 8.549 8.8656 8.6405 8.2635 8.758 8.517 8.9638 8.2835 8.751 7.76915 7.94555 7.83845 8.4157 7.65095 7.93905 7.531 8.45985 7.64365 7.9037 7.72355 7.354 7.8644 7.5746 7.8134 7.9135 7.4153 8.7034333 8.3874 8.253 8.3171 7.76055 8.9284 7.7228 7.7079 8.2991333 8.7202 8.5742667 7.9661667 7.9347333 8.4113 8.2549667 8.1664 7.8362 8.238 8.893 7.9627 7.92 8.2434 8.1038 7.9922 7.4875 7.2641 8.5304 9.0243 8.301 8.9671 7.8267 7.8892 7.775 7.4941 7.6238 7.45 8.3163 8.2447 8.0018 7.93 7.4296 8.0405 8.3143 8.0761 8.0868 8.0816 8.0593 7.9434 8.5384 8.2426 7.8977 8.0362 7.97275 7.70055 8.7639 7.9547 7.8581 8.2419 8.0132 8.619 8.0371 8.0116 8.2792 8.0318 +"G2643" 11.014 11.1523333 11.166 9.39395 10.432 9.962 11.1756667 11.013 11.2663333 10.9335 11.0325 9.6975 10.00895 9.326 9.8026 11.143 9.4706667 10.3985 9.423 11.119 10.1695 10.6886667 10.188 9.3628 10.689 10.4166667 11.2844 10.324 10.5552 10.096 10.259 8.6475 11.371 10.3226667 10.745 10.594 9.54385 9.7734 10.6344 8.2753 10.1471 10.728 9.2416667 10.702 9.7894667 9.6973 10.6496667 10.445 10.549 9.6329333 9.1804 10.9125 10.162325 10.0726667 9.7881 10.8453333 10.9525 9.682 8.6973333 10.234 9.4833667 9.6973 10.203 10.457 9.4147 10.7346667 9.1804 9.5733 8.8888 11.199 10.0415 10.3815 10.4236667 11.3898 11.4338 10.557 10.3324 10.81 9.17425 10.62 8.8524 11.393 10.5173333 10.366 10.5556667 10.4993333 10.572 10.3576667 9.3965 10.803 8.8024 8.4445 9.66335 10.4666667 10.661 10.417 10.61 9.2867 9.8653 9.8771 10.198 10.0745 10.894 10.678 10.837 8.8247333 10.975 9.8176 11.084 10.279 11.006 10.212 10.165 10.235 10.041 11.093 10.893 11.037 11.087 10.56 9.9476333 10.373 10.624 10.38 9.145 10.49325 10.309 10.566 10.249 9.3723 10.283 10.674 9.0135 10.877 11.2595 10.3686667 9.9278 10.0753333 9.9676333 10.298 10.5376667 10.2305667 9.379 10.581 11 11.044 11.592 11.2395 9.7332 9.6167 10.516 9.7729 10.654 10.508 10.917 10.585 10.4256667 8.9397 9.6585 10.438 10.6305 10.717 10.147 10.849 10.8545 10.2685 9.4238333 9.3828667 9.3362 10.5556667 10.7856667 10.1690333 9.3839 9.1714 9.3874 9.7677 10.182 9.0521 9.5586 10.6385 9.5772 9.63345 9.83065 9.7225 9.46995 10.3655 9.7779 9.16355 9.296 9.4183 9.8494 9.9059 9.1863 9.1355 9.292 9.7238 9.3587 9.77 10.443 9.643 9.3665 9.0383 10.957 10.913 10.799 10.907 10.792 11.048 10.991 10.684 10.659 10.863 10.745 10.541 10.911 10.706 10.57 10.309 11.467 11.235 11.209 9.7752 11.064 9.4163 9.2858 9.8604 9.7513 9.3429 11.795 11.408 10.85 10.774 11.359 10.505 10.995 11.6186 11.4936 11.3242 11.1442 11.517 11.465 11.436 11.486 11.535 10.645 10.492 10.557 10.861 11.435 11.489 11.59 11.546 10.39 10.316 10.402 10.28 10.193 11.742 11.531 11.368 11.112 11.629 11.425 11.64 11.403 11.623 11.392 11.802 11.56 11.544 11.491 10.882 11.378 9.9136 10.623 10.957 11.295 10.254 10.828 11.094 11.106 11.143 10.967 11.089 10.648 11.4276667 11.0333333 10.7793333 10.6533333 10.319 10.258 11.12 10 9.8445 9.9539 10.147 9.5184 9.0205 9.326 8.2753 8.9382 9.907 9.1289 9.6288 8.7891 8.7542667 9.6204333 10.0302 9.9902667 8.9450667 8.8761333 8.7366333 8.6686333 8.894 9.9828 9.441 9.1165 10.777 10.983 11.509 11.563 10.768 9.6572 9.239 9.4554 11.453 11.793 11.527 11.531 11.642 10.747 11.017 11.051 10.411 10.48 10.489 11.583 11.372 11.374 10.687 9.7593 10.406 9.6712 11.5306667 11.381 11.044 10.92025 11.405 11.648 11.3765 10.9253333 11.6335 11.556 10.861 11.1226667 11.5093333 11.4726667 11.0743333 11.0875 8.7891 8.7542667 9.6204333 8.8761333 8.7366333 10.01064 9.9902667 8.9450667 8.6686333 8.894 10.653 10.775 10.741 9.7175 10.319 10.299 9.5471 9.1809 10.932 10.871 10.654 10.651 11.214 10.872 10.439 10.72 10.736 10.793 10.811 10.505 10.191 9.588 10.248 10.173 10.243 9.6948 9.4393 9.7599 10.668 11.012 10.937 10.66 10.942 10.417 9.8043 10.087 11.134 11.26 11.0065 11.3495 11.2785 11.1635 11.129 10.9675 11.0195 11.363 10.9055 11.3195 10.124 9.7881 9.1804 9.5861 9.6222 9.3107 10.536 10.783 9.88965 9.5406 9.5452 10.669 9.7177 9.8837 10.9603333 10.5813333 9.968 9.6149333 11.1056667 10.567 10.0523333 10.0476667 10.779 10.685 9.1744 10.295 10.622 10.866 10.606 9.6333 10.382 9.943 10.775 10.566 10.368 10.12 9.9701 10.975 10.558 10.811 10.763 10.824 10.788 10.566 10.668 10.12 9.6874 10.923 10.941 10.5 10.878 10.706 11.346 10.749 10.954 10.492 11.3385 11.0235 11.002 10.603 10.604 11.013 10.55 10.18 8.9606 10.463 9.9466 10.203 10.117 10.299 +"G2644" 9.37115 9.7093 9.9951 9.05075 9.38735 7.6734 9.7101333 8.6241 9.0901 8.6505 9.7612 9.5538 9.2231 7.0394 8.6928 9.72305 7.5364 9.6276 8.1003 9.235 8.4148 8.5480333 9.164 8.0188 9.8323 8.9939 9.15846 10.301 8.7956 8.6583 8.7468 7.8064 9.2804 9.8388 8.7382 9.0755667 8.34305 8.68465 9.6245 7.2732 8.6609 8.9464 7.5543667 8.2799 8.2939333 8.2815 9.1980667 8.8453 9.0908 9.2249333 7.8859 9.61585 9.7846 7.8585667 7.3955 9.8459333 9.19725 8.3113 8.9057 9.1761 8.5409667 8.2815 8.1514 8.25715 9.0019667 9.0582 7.8859 9.4802 7.8505 9.6242 9.48935 9.32015 9.3494333 8.86688 9.23622 10.227 8.86304 8.87206 8.5779 8.9205 7.6388 9.2979 9.6702667 9.6861 9.537 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8.0836 8.1303 7.9234 8.184 7.737 8.8141 7.6879 8.329 8.6534 7.9924 8.3742 8.4829 8.2297 8.2898 8.52 8.2927 8.1456 8.1203 8.6453 8.2803 8.1621 8.026 8.4201 8.3141 8.1475 8.0038 9.0436 8.7724 8.503 7.9438 8.201 8.4103 8.3757 9.4059 9.1101 9.1381 7.9342 8.1015 8.0978 7.9063 8.3084 7.6506 8.4391 9.47494 9.0901 9.12178 8.99706 8.7451 9.0667 8.8826 8.6915 8.3049 7.7745 7.9848 8.163 7.9673 8.5581 8.5798 8.3913 8.3418 8.0871 8.1831 8.2767 8.4501 8.4171 9.0663 8.6695 8.6061 8.3182 8.3542 8.3791 9.0143 8.3478 8.3815 8.2742 9.14 8.4759 8.4187 9.3441 8.4541 8.4344 8.2993 7.6394 8.2902 8.4582 8.2434 8.2686 8.7932 8.9777 9.0578 8.7104 8.5193333 8.2358 8.2853333 8.3701333 8.0030667 7.7328667 7.7078667 7.8954333 8.4828 9.1545 9.4642 9.4701 8.0581 8.8339 8.5359 7.535 7.7565 7.3588 7.5604 7.9922 7.2018 7.0225667 7.0982667 8.3758333 8.2203667 7.9702 7.7868 7.5135 7.3051667 7.3126 7.6299667 8.2013 8.3129 8.1527 7.8505 8.758 8.2689 8.7469 8.7633 8.6488 8.909 9.1901 8.2918 8.8316 8.6333 8.2011 8.626 8.4575 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9.2813 9.6346 10.13 10.392 9.8812 10.171 10.237 10.696 10.241 10.575 9.6701 9.9205 10.597 10.362 10.301 10.164 9.7127 10.419 10.542 10.36 10.559 10.38 10.776 10.552 10.642 10.473 10.761 10.449 10.7095 10.16195 10.623 11.017 10.837 11.212 11.19 10.668 11.999 10.893 11.47 10.475 +"G2851" 8.049 7.6192 7.8198 8.41005 7.9191 8.0694 7.7594667 7.56885 7.4581333 7.51075 8.0508 8.3028 8.11435 7.4939 8.8196 7.6376 8.3335333 8.17025 7.3705 7.4988 7.81725 7.3115667 8.1387 7.6981 7.595 7.9111667 7.87524 7.7184 7.51254 7.82905 7.9328 7.21 7.6438 7.5455 8.0129 8.2466667 7.77465 7.66725 7.97844 8.3234 7.9458 7.4141 8.3468667 7.1266 7.8556333 7.6508 7.9352333 7.6167 7.565 7.5965 7.9705 7.5695 7.505875 8.1249333 7.0127 7.2575 7.82575 7.5764 7.66 7.7614 8.432 7.6508 7.8768 7.3316 7.6888333 7.4538667 7.9705 7.7929 7.9475 7.6952 8.2036 8.11075 7.9372667 7.87532 7.77024 7.4906 7.54498 7.54884 8.28445 7.4459 8.0732 7.6686 7.2312667 7.4894667 7.2687 7.2143 7.1662667 7.3758667 8.1036 8.1307333 7.7451 8.2236 7.8907 7.9269 7.6249 8.0834333 8.1772 7.8594 7.8089 7.5577 7.9923 8.0382 7.5768 7.6161 7.4807 8.389 6.8883 7.5032 7.3987 7.6441 7.4653 7.4379 7.9922 7.6149 7.2787 7.3925 7.8371 7.4989 7.1625 7.8945 7.7842 7.7086667 7.8497667 7.6657 8.5612 7.843475 8.0514 7.7674 7.377 7.7846 7.2652 7.3613 7.4718 7.6707 7.7776 7.3742667 7.3833 7.5050667 7.2442333 7.2610667 7.3061 8.0556667 7.4419 7.5960333 7.7133 7.8092 7.6765 8.09265 7.1023 7.5482 7.4686 7.5501 7.5559 7.7979 7.7978 8.1597 7.6722 8.1626 7.7682 7.2802 7.1433 7.30115 7.79075 7.5132 7.73605 8.0332 7.3638333 7.5097 7.3204333 7.7178667 7.7484333 7.7745333 8.2903 7.6478 7.48765 7.7033 7.6397 8.1787 7.4781 7.5303 7.65685 7.5465 7.80625 7.62255 8.2677 7.5892 7.63265 7.7481 7.7073 7.60785 7.6908 7.4271 8.0962 8.2661 7.9275 7.4935 7.6724 7.6361 7.6776 7.9569 8.1644 8.3489 7.6528 7.9832 7.7639 7.7599 7.7378 7.8033 7.838 8.3884 7.5067 7.69 7.7229 7.6573 7.6298 7.7565 7.6738 7.8669 7.4197 7.4194 7.3248 7.7578 7.7445 7.6149 7.8164 8.0148 8.0401 8.7296 7.7499 7.6532 7.5366 7.3654 7.8244 7.5648 7.505 7.6918 7.6038 7.48178 7.57104 7.3626 7.5902 7.3517 7.2926 7.4622 7.5294 7.4834 7.3584 7.7269 7.6053 7.5699 7.4875 7.3887 7.4848 7.3245 7.3442 7.4922 7.6326 7.5011 7.6883 7.6254 7.6039 7.4564 7.5728 7.5434 7.7446 7.6431 7.77 7.5603 7.4869 7.5678 7.6099 7.5823 7.7057 7.8827 7.4198 7.4064 7.5537 7.5871 7.541 7.5916 7.5234 7.53 7.7518 7.5040333 7.3515333 7.5486 7.7457333 7.6135333 7.6474333 7.8022667 7.8982 7.5094 7.4407 8.3295 8.8347 9.2024 9.4018 8.9299 7.4939 8.3234 7.3895 8.3766 7.5519 7.9601 7.7989333 7.5968333 7.8799333 8.1130667 7.9235 8.4350333 7.9378667 7.5893 8.3076 8.0842333 7.6525 8.1412 8.0473 7.3196 7.6094 7.2523 7.1308 7.4779 7.7165 7.6789 7.8181 7.7511 7.5055 7.5319 7.5287 7.5184 7.7731 7.5027 7.6866 7.5102 7.5106 7.6058 7.6672 7.5155 7.4772 7.6638 7.732 7.7404 7.9578 7.7948667 7.74065 7.7133333 7.770125 7.91125 7.68135 7.6813 7.8530333 7.83125 7.82485 7.75635 7.7833333 7.7083667 7.6657 7.8513333 7.728275 7.7989333 7.5968333 7.8799333 7.9378667 7.5893 8.0451 7.9235 8.4350333 8.3076 8.0842333 7.166 7.3257 7.5023 7.5515 7.6105 7.7384 7.6125 7.7687 7.6224 7.6836 7.9434 7.764 7.5772 7.6142 7.73 7.5537 7.6548 7.5894 7.7265 7.1062 7.9002 7.8654 7.788 8.1004 7.7389 7.8998 7.7341 7.934 7.6745 7.5679 7.3768 7.4959 7.4861 7.3063 7.4512 7.4081 7.2363 7.25615 7.09445 7.21655 7.08795 7.5091 7.2001 7.48705 7.31395 7.41205 7.1454 7.4179 7.4353 7.0127 7.9705 6.9132 7.9965 7.9152 8.0998 8.4837 8.3209 8.7998 8.64515 8.1122333 8.47095 8.6469 7.8639667 8.1175667 7.9951667 8.0706333 7.7732333 7.8956667 7.6753667 7.6760333 7.403 7.4751 7.3068 7.4473 7.3175 7.3163 7.432 7.5153 7.6411 7.868 7.5189 7.5587 7.586 7.7955 7.6689 7.2826 7.4811 7.5013 7.3492 7.4329 7.5563 7.7409 7.7006 8.3811 8.2998 7.7484 7.6553 7.6096 7.7407 7.8565 7.8645 7.6395 7.9081 7.8447 7.67625 7.4428 7.77615 7.835 7.454 7.473 7.3646 7.7229 7.4148 7.5748 7.8984 8.2811 7.7284 8.4023 +"G2852" 9.4232 9.4455333 9.7678 9.0842 8.82685 8.1481 9.1972333 9.0768 9.3136667 9.05785 9.841 8.7736 8.9233 7.6183 8.3784 9.3 8.4820333 9.14695 7.6652 8.9985 8.9737 8.8320333 8.99145 7.929 9.357 8.5032667 9.26086 9.1946 8.94974 9.17825 8.7988 7.8629 9.2274 9.3633333 9.3897 9.1661 8.3205 8.8549 8.77024 7.9838 8.63815 8.6983 7.8256 8.0934 8.5879667 8.5855 9.0508 9.0149 9.5021 9.1050333 7.8897 9.0656 9.4985 8.2541333 7.9972 9.1425667 8.68825 8.9983 9.7412667 8.3531 7.9701 8.5855 8.335 8.32875 8.5987667 8.3351 7.8897 8.5875 7.9268 9.1864 8.8202 8.8747 8.8864 9.48266 9.36348 9.1204 8.87714 8.9532 8.6632 8.7644 7.7342 8.9151 9.2344333 9.2379 9.1901333 9.3411333 9.2333667 9.2739667 7.8258 9.4131667 8.6582 8.1163 8.82715 8.8769 8.9609 8.3423 9.0585 8.0323 8.3772 8.6361 8.4883 8.6007 8.6028 9.1306 8.9277 7.7352333 8.301 8.618 9.0126 8.463 9.0576 8.7898 8.6358 8.5339 8.0769 9.2446 9.0348 9.0465 8.6082 9.0435 8.2776333 8.2857333 8.8470333 8.6543333 8.3687 8.818075 9.2854 8.9827 9.0502 8.9887 8.9122 8.9752 7.9905 9.1597 9.7258 9.5513667 9.1389333 9.3697 9.2012333 9.4369333 9.2572333 8.2631 9.2579 8.8268 8.7553 8.5453 9.145 8.92915 8.8224 9.653 8.8041 8.7158 8.9947 8.6574 8.8042 8.4397 8.8137 7.8741 7.957 8.52475 7.8431 8.67655 8.40815 8.57125 8.4951 8.8545 8.5629333 8.8717 9.1362 8.4903667 8.6604333 8.5901667 7.7113 8.2322 7.6492 9.0252 8.6688 8.1814 7.8417 9.39535 8.92315 8.95315 9.00105 9.10725 8.608 8.8538 8.73275 8.59875 8.6651 8.67405 8.9549 8.1121 8.4252 8.3938 7.6563 7.6799 7.6441 8.4285 8.9997 8.903 8.8296 8.2691 9.4124 9.5498 9.6084 9.3571 9.4267 9.5232 9.0726 8.651 9.653 9.4752 9.3503 9.0172 9.2222 9.2026 9.3759 9.0771 9.1753 9.3181 8.9299 8.989 9.1389 8.4744 8.3627 8.2338 8.0475 8.3623 9.2905 9.0493 8.8615 8.4098 9.3715 8.6847 8.7401 9.5997 9.20988 9.20332 9.16036 8.7798 9.4451 9.2447 9.4561 9.2856 8.9864 9.3882 9.1151 8.7586 9.0767 8.9947 9.0761 9.3243 9.4157 9.32 8.8069 9.0408 8.7241 9.5514 9.6855 9.6546 9.4157 9.2191 9.5723 9.7091 9.2942 9.3765 9.1754 9.7653 9.5062 9.4635 9.8419 9.2425 9.3324 8.6894 8.8191 9.1673 9.2116 8.6832 8.7533 9.4953 9.4778 9.0273 8.9419 9.3560333 9.0453667 9.2768333 8.2178667 8.5185667 8.5569667 8.5075 8.3516 9.4172 8.07 8.7628 8.6186 9.4687 9.3016 8.9017 7.6183 7.9838 7.7436 8.468 8.0691 7.6795 8.2376333 8.3429 8.9111 8.6001333 8.4366333 8.4848 8.6157 8.5689333 8.4471 8.2006333 7.732 8.1728 8.2018 8.4617 9.058 9.2865 9.2115 9.184 8.5128 8.6419 8.7358 8.9475 9.573 9.1528 8.6333 8.9803 8.9619 8.8297 8.9963 8.1089 8.1606 8.2685 9.3698 9.3749 9.3291 8.959 8.7812 8.9874 8.858 9.7956667 9.74825 9.4298 9.21155 9.48135 9.4895 9.31 9.2899333 9.5455 9.471 9.2082 9.2919 9.6248333 9.7225333 9.4278667 9.3715 8.2376333 8.3429 8.9111 8.6157 8.5689333 8.43536 8.4366333 8.4848 8.4471 8.2006333 8.9411 8.9852 8.8802 8.2331 8.3055 9.0086 8.2706 7.7298 9.2907 9.1194 9.1248 9.104 9.2355 9.1155 8.6279 8.5121 8.6283 8.916 8.6017 7.9407 9.1255 8.4739 7.8833 7.9232 8.4151 8.2506 8.0704 8.2345 8.5774 8.9647 8.4612 8.8377 9.0922 9.2958 9.3439 8.5951 9.34735 10.01155 9.264 9.3095 9.118 9.7035 9.43505 9.4474 9.1641 9.6459 9.2188 10.0304 8.0436 7.9972 7.8897 8.2142 9.1379 8.7533 8.9582333 8.3843 8.58775 8.36755 8.52115 9.0424333 8.4376 8.2938 8.9988333 8.9225333 8.9288667 8.8558 9.2541333 8.7142 8.5241667 8.4763 9.4592 9.2216 9.1783 8.7861 9.2087 9.4811 9.2195 9.1927 8.4542 8.7183 9.0904 9.5543 9.0378 8.8884 9.3152 9.6767 9.3732 9.641 8.8047 9.3566 9.4115 9.5139 9.0921 8.506 8.3904 9.3652 9.4376 9.309 9.5346 9.4535 9.3792 8.5693 9.0888 8.8216 9.26675 9.23485 9.31785 9.25855 9.0894 9.0879 9.2198 9.3679 9.9979 9.0955 10.061 9.2398 9.2829 8.6714 +"G2853" 9.08515 10.0165667 9.61025 7.21535 9.0277 7.6972 9.6308333 9.3437 9.3674333 9.02485 9.6512 8.9067 8.40925 7.4414 7.7778 9.33405 7.4197333 8.70975 7.2692 9.0198 8.30105 8.4364667 8.68805 7.0939 9.1838 8.7235333 8.8203 9.6352 8.63054 9.00165 8.1692 7.3841 9.4029 9.1071667 8.0359 8.7756333 7.39395 8.1456 9.08872 7.6304 7.6926 8.8427 7.8345333 8.528 6.9196667 7.8622 8.3951 7.9013 8.5233 8.4389 7.6801 8.4958 8.662075 7.8997333 7.4778 8.9138 8.19105 8.0347 9.6313333 7.9004 7.4008333 7.8622 8.2618 8.1427 8.0990667 7.9539333 7.6801 8.2042 7.2624 8.9698 8.2434 8.58055 8.892 8.7361 8.81526 9.4595 7.5835 8.65056 7.9665 8.9294 7.2916 8.9225 8.4248333 8.9336667 8.9862333 9.1266667 8.9284 8.7738333 7.7891 8.8559667 7.5645 6.8056 8.0895 9.5971667 9.145 8.8038 8.8955 7.3156 7.6834 8.1889 8.0275 7.9781 8.7691 9.0204 8.6215 7.909 8.7922 8.257 8.7395 7.8628 9.3774 8.8872 8.0257 8.0321 8.1967 9.2962 9.0552 9.1895 8.6857 8.5823 7.593 7.8372667 8.8944 8.2896667 7.3583 9.0522 9.0143 8.722 8.598 8.0522 8.1407 8.5559 7.0714 8.2875 9.75665 9.0236 9.0342333 8.8105667 8.9494 8.8103 8.7335667 7.8347667 7.174 8.3750667 8.5417 7.9339 8.7054 8.42565 7.9867 10.095 8.3924 8.0691 8.5209 7.7273 8.036 8.4813 8.0387 7.4764 7.5608 8.2446 8.2618 8.7663 7.9359 8.3597 8.3838 8.56775 7.8554333 7.5748 8.0628 7.3936667 8.1299 7.9921 7.7461 7.1138 7.89975 8.5995 8.104 7.8474 8.9427 8.2202 8.5087 8.32035 8.27785 7.7903 7.3439 8.1295 8.50925 8.29885 8.4993 8.62335 8.0992 7.5924 7.0686 7.1513 7.6707 8.6065 7.6567 9.0996 8.15 7.7162 7.4904 7.1797 8.8753 8.8661 8.9183 9.0806 8.9536 8.5286 8.4526 8.1029 8.9847 8.9439 8.9049 8.613 9.0288 9.332 8.8754 8.2288 9.0192 8.8558 8.9785 9.3122 9.2009 9.4362 9.1732 10.005 9.7917 9.8148 8.2822 8.4365 8.1093 7.7044 8.7136 7.4681 7.9295 9.51488 9.23998 9.23852 9.2277 8.9942 9.082 9.0891 9.0963 9.0974 9.0278 9.0226 8.7664 8.3153 9.1038 9.0014 9.02 8.8893 9.4771 8.8391 8.8162 8.6414 7.8357 8.9451 8.9989 8.9263 9.4882 8.7089 9.1592 8.8946 8.9475 8.7855 8.7595 8.9325 8.824 8.9595 9.4273 9.0393 9.0646 9.4459 8.1344 9.0993 9.1053 9.5457 8.1757 9.1487 9.3498 9.1531 8.9264 9.2427667 8.8790333 8.9224 9.6812667 9.7679333 10.0068333 9.6866333 9.3922 8.8044 9.6109 9.6987 9.5357 9.755 9.8138 9.3453 7.4414 7.6304 7.6217 7.7014 7.627 7.4656 7.2017 7.1204 7.1182 7.1593333 7.0275 7.5228667 6.9352333 6.8855 7.5322333 7.3205 7.4419 7.417 7.4792 8.394 8.8508 9.2813 9.079 9.2011 8.9568 9.7409 9.8554 8.5106 9.2071 9.0451 8.6052 8.5801 8.4991 9.1772 8.9897 8.2181 8.084 8.27 8.8838 9.4594 9.5613 9.3303 9.4089 9.4573 9.4627 9.5503 9.4967 9.5021667 9.43095 9.30255 9.25935 9.3066 9.0860667 9.2416 9.2625 8.99465 9.1482667 9.4540333 9.5322 9.3520667 9.5364 7.2017 7.1204 7.1182 6.9352333 6.8855 7.15584 7.0275 7.5228667 7.5322333 7.3205 8.2018 8.1474 8.7064 7.9632 7.6811 8.2947 7.8597 7.2607 8.9581 9.471 8.6037 8.9951 8.4806 8.828 8.6276 7.9676 8.4302 8.7312 9.3142 8.226 9.4434 9.3406 8.9901 8.8361 8.6095 9.7444 9.7533 9.2128 8.6708 8.5443 8.7309 8.9457 8.8725 9.812 9.6669 10.016 9.27145 9.9098 9.57445 9.8311 9.3807 9.8791 9.7268 9.9148 9.1259 9.8888 9.6127 9.9852 7.8767 7.4778 7.6801 7.5086 7.2345 6.7916 8.8407333 7.7084 7.6054 7.10765 7.3155 9.0370333 7.1147 7.0729 8.5553333 8.4905667 8.0334333 7.7151 9.2032 9.2268333 8.8417 8.4107 8.707 8.6647 8.5242 7.6468 7.8586 8.4732 8.6783 8.0638 7.3608 7.6636 7.7433 8.4884 8.252 8.3863 7.628 9.1763 8.9603 9.1257 8.9733 9.1695 8.8103 8.7335 8.5232 7.7511 7.3136 8.9178 8.8529 8.8398 8.6755 8.5793 8.93 8.4274 8.9487 8.4465 8.99245 8.94075 9.231 8.74165 8.9283 9.4778 8.6626 9.6389 9.5192 8.7478 10.438 9.4978 10.199 9.9073 +"G2854" 9.44165 8.4382 9.5635 9.6859 9.5796 8.8191 8.0478667 9.17025 7.9768 9.02995 9.39815 9.9657 9.81455 8.6235 9.0285 9.17885 8.7767667 9.6204 9.5985 8.9222 9.5185 8.0053667 9.6896 8.7523 9.3468 9.7559667 9.3823 9.2971 9.316 9.56495 9.7566 8.6406 9.2172 9.0648 8.9568 9.4974667 9.3232 8.9497 9.67206 8.6803 9.2963 8.9368 8.9667 9.2847 9.3420667 8.9653 9.3927 9.3301 8.8045 9.2806667 8.4214 8.95565 9.04695 8.9078 8.0805 8.738 9.02175 8.9495 9.1424333 8.9146 9.7127 8.9653 9.0887 8.71785 9.3395 9.1404667 8.4214 9.0364 8.8168 9.3198 9.66135 9.5108 9.6168667 9.21418 9.25212 9.2489 9.19904 9.31412 10.29 9.2816 8.4493 9.4083 8.9770333 9.0331333 8.8399333 8.8940667 8.8559333 9.0634667 9.3101 9.4849 9.2315 8.7039 9.01915 9.4210667 9.3527 9.3639333 9.6605 8.9056 9.0266 9.0014 9.0427 9.1264 9.3411 9.2116 9.2818 8.8376667 8.8767 8.9863 9.1203 9.2512 9.4794 9.2544 9.3841 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8.9375333 10.1909 8.8672667 7.4483333 7.4596667 8.2471667 7.4162667 7.3554667 7.0201 7.2136 6.9276 7.2302 7.1983 7.1182 6.7458 6.6482 6.9755 6.8284 9.4258 10.348 9.8657 9.8262 7.4463 6.9948 6.8993 6.9897 6.8496 6.9254 10.652 9.8701 9.2123 7.7092 7.3425 9.8359 10.605 10.342 10.223 10.579 9.0596 9.7644 10.297 8.3238 9.38025 9.8218 8.33465 7.27765 10.968 8.5962 7.6614 9.4753 9.0354 10.589 9.0604 7.5029 8.3005 7.2101 +"G2931" 10.8325 11.5553333 11.737 11.9115 11.684 11.183 11.688 10.843 11.1683333 12.214 10.6585 11.489 11.6605 11.691 12.674 10.37775 10.9413333 10.863 10.412 12.67 12.5105 11.4453333 11.278 9.9263 11.991 11.3273333 12.264 11.385 11.5464 11.504 11.792 12.176 10.744 11.5716667 10.836 10.9983333 11.2785 11.554 12.2168 11.138 11.0135 11.176 11.689 10.656 11.4596667 11.305 11.4813333 11.787 11.263 11.7976667 11.025 10.51235 10.820425 11.5166667 10.799 11.4126667 11.41 11.338 11.2596667 10.663 10.5859333 11.351 11.034 11.81 12.4336667 11.6456667 10.876 11.046 12.248 11.488 11.707 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9.6072 9.436 9.4883 8.9583 9.3772 9.5222 9.67945 8.95775 8.9317 9.06805 10.161 9.7211 10.33 7.3819 8.1119 10.172 8.3024 8.7 9.8058 9.5583 +"G3286" 9.0212 8.1209 7.74155 9.90555 8.62205 12.374 9.1521 8.2005 8.5431667 8.3801 7.9731 8.3064 8.33465 11.568 8.0296 9.63895 8.7951 10.2005 8.5162 8.2896 11.6515 10.5106333 9.25545 8.448 8.299 8.2240333 9.8803 12.037 8.95338 11.38 8.2167 8.5253 10.45 10.6111667 11.352 9.0428333 8.25595 8.2326 9.78688 8.4164 10.27425 8.0164 11.0347 8.2259 8.1233667 7.9591 10.5567333 7.6633 12.485 9.3802667 10.932 8.33795 10.363875 9.3780333 8.4695 9.4315667 10.1754 8.1524 7.1223667 11.864 8.0589667 8.3293 12.317 10.1622 10.1988333 8.7214667 8.189 8.9789 8.0788 7.9389 8.27225 8.2883 9.4400667 9.23728 10.20042 8.0788 10.29026 9.1588 11.8095 11.522 7.9829 8.1392 9.6887 8.4625 8.3614 7.8748333 10.1580333 8.1537 8.2321 8.8849667 7.9706 7.8553 9.2944 9.1817667 8.587 7.9387 12.079 8.0994 12.183 8.1863 8.2132 6.94045 8.0883 7.9124 8.6399 9.528 8.3063 11.417 11.649 11.4 8.5874 11.775 11.691 8.4093 8.0883 12.413 8.154 10.034 10.932 11.864 9.3392333 9.317 9.2381333 8.1903667 7.669 9.078975 8.1895 8.5368 8.585 8.4128 11.864 8.2043 8.5636 7.7444 8.1364 8.0576 9.3417667 9.3925 9.4218 9.2000667 7.8659333 9.3268 8.0674 8.7923667 7.9947 7.9208 8.0051 8.01235 7.7974 7.5264 8.1646 8.6905 12.512 8.1778 7.596 8.0532 10.7134 11.314 8.2993 11.9685 12.1015 9.9753 8.00105 8.74495 9.6094 9.8856 8.0484333 8.2997667 9.8587 8.2324333 10.1659 8.0425667 8.0881 10.052 8.07745 8.7667 8.0287 11.704 11.57 8.57855 10.1087 8.03895 8.32545 8.1513 7.80205 9.42025 10.42345 7.7495 8.03955 8.68155 8.0394 11.831 12.283 8.2478 8.4179 7.9216 10.55 8.7385 8.2089 8.0775 7.9912 8.0563 12.345 8.5874 11.093 10.471 8.0994 8.3392 8.1385 7.7492 9.1076 8.4146 5.4888 11.864 7.7527 11.049 8.4845 12.171 9.4351 5.1562 8.3958 8.9204 12.179 8.2656 7.7073 12.247 11.874 8.557 8.8431 8.0995 8.5636 7.9065 12.171 8.369 8.3348 8.62798 9.48256 8.62466 10.9226 8.4476 7.4718 8.3034 9.2736 8.2106 9.0035 7.766 8.4037 11.352 8.473 8.184 11.417 7.9496 8.2243 10.72 7.9591 8.6098 11.785 12.079 12.172 7.9762 8.1072 8.1393 8.2101 12.374 8.423 7.9136 11.314 10.965 11.691 10.855 10.662 8.585 12.246 8.0887 7.9496 8.6905 10.907 8.0994 7.8029 8.369 12.002 7.7527 8.2857 7.8801333 11.83 10.6432 8.9234333 9.1322667 10.5017333 9.3042 8.4609667 8.5154 12.367 8.2006 9.2812 12.345 7.9935 7.4865 12.097 8.0332 8.1908 7.9168 8.9986 11.443 9.4817 9.1787667 9.1796667 8.0203667 9.7231 8.2236333 9.0516 8.1459333 9.4107667 10.5537 8.3529 8.4128 8.0349 7.7679 11.874 8.6565 5.4013 11.57 11.164 11.314 11.631 10.522 10.855 8.9986 12.553 8.2889 12.283 8.3293 8.3001 12.528 11.519 7.8271 10.45 7.9935 8.5177 11.519 8.0394 12.323 11.4 8.9574667 10.113 10.4252333 9.077325 10.1045 9.90215 8.46075 10.3157333 8.04405 11.74 8.68705 8.1324333 10.2972667 7.9052 9.3905 9.19225 9.7986667 8.2225667 9.4349 9.5503667 8.8335 9.53454 9.9699333 8.1201333 10.3208667 8.3357 7.9168 12.304 11.753 11.192 9.7315 11.707 11.591 12.368 8.1643 11.605 8.5488 7.7781 8.1644 11.66 11.649 8.3133 7.596 12.374 10.27 7.7908 8.4037 8.4695 7.7781 10.716 11.597 12.085 12.07 8.1524 12.344 9.0001 12.521 8.4302 8.2771 11.285 8.1101 8.0674 9.90995 9.61785 11.8025 7.95865 9.9456 8.2972 8.2176 8.5286 8.40245 10.0389 9.7219 8.6116 11.724 7.9227 8.368 8.1822 8.3001 8.3157 9.5769 10.3741 8.2396 8.25715 8.01605 8.1984 11.359 8.0532 9.2482667 9.2642333 8.4603 9.9130333 10.3339667 8.0226667 9.5557333 8.2011333 11.416 9.0381 8.0875 7.9496 11.211 8.2319 8.184 8.5195 8.356 7.9299 8.2531 10.568 8.4377 11.783 12.157 8.3911 12.224 12.528 7.8752 11.475 8.1638 9.8298 11.66 11.729 7.6633 11.285 12.374 8.0994 5.4888 8.2292 9.1224 11.649 8.369 8.145 9.93875 8.3438 10.19655 7.95085 12.246 11.416 11.019 8.4164 7.9935 7.9833 12.222 8.201 8.4164 8.4038 +"G3287" 7.24595 8.0918 7.19955 8.7289 7.4144 7.1959 8.2630667 7.1771 7.9810333 7.28395 7.2907 7.9112 7.30175 7.3434 7.4258 7.31875 8.1895333 7.40275 7.1831 7.48145 7.3262 7.9429333 7.6345 7.3125 7.596 7.4590667 7.42806 7.7156 7.0633 7.0296 7.8136 8.0199 7.5825 7.3768 7.6285 6.8526 7.87995 7.2127 7.3012 7.316 7.91765 7.2642 7.1835 7.2794 7.5983333 7.7309 7.2514667 7.4461 7.2903 7.3015 7.4994 6.93745 7.194575 7.0485667 7.1975 7.4303 7.16015 7.2612 7.2333333 7.2283 7.9281667 7.7309 7.9959 6.5106 7.4856667 7.1156333 7.4994 8.1152 7.7033 7.3412 7.24725 7.2592 7.4210667 7.3271 7.23544 7.3607 7.10954 7.28568 7.222 7.4816 6.972 7.558 7.2855667 7.2012 6.9525 7.2075333 7.1039 7.1353 7.9959 6.9338 7.5105 7.9424 7.508 7.2757 7.2052 7.1565667 7.7412 7.4432 7.7771 7.6688 7.6743 8.1149 7.433 7.2481 7.3959 7.5857667 7.316 7.0051 7.3348 7.2717 7.6166 7.2895 7.4602 7.1873 7.0919 7.2303 7.6089 7.315 7.0293 7.5146 7.5809333 7.2485333 7.4379 7.5844667 7.7293 7.342875 7.0687 7.1392 7.1228 7.6393 7.4568 7.3293 6.8824 7.1566 7.40105 7.1289667 7.3037667 7.1959333 7.3934 7.0301667 7.1546333 7.0913333 7.3703 7.5555 7.3308 7.4228 7.4788 7.3403 7.3981 7.2141 7.2117 7.025 7.3039 7.0999 7.2203 7.4297 7.3372 7.5296 8.1601 6.40125 6.65605 6.60145 7.31565 7.256 7.18435 7.22595 7.1729667 7.2543 7.1582 7.2616667 7.2647333 7.1639333 7.5143 7.4615 7.4424 7.3944 8.1252 8.4187 7.1068 7.032 7.57495 7.45335 7.67115 7.5812 7.4659 7.0737 7.2662 7.19485 7.40225 7.421 7.2752 7.5694 7.5462 7.5679 8.2733 7.1574 7.8988 7.4805 7.73 7.6511 7.6036 7.6394 7.0855 7.1177 7.2144 7.0451 7.0909 7.1515 7.1532 7.4233 7.0898 7.2672 7.336 7.2842 7.0147 7.2601 7.1286 7.4234 7.3682 7.1915 7.3717 7.716 7.5725 7.3575 7.2171 6.7015 6.7666 6.5873 7.4975 7.3615 7.6885 7.6711 7.2192 7.9118 7.7677 7.0729 6.99676 7.06906 6.98698 7.3641 7.1999 7.2178 6.9894 7.2666 6.5982 7.226 7.1785 7.5474 7.2325 7.2359 7.2867 7.1456 7.2495 7.1087 7.1507 7.2204 7.4713 7.3612 7.196 7.1552 7.3286 7.1256 6.9475 7.0548 7.098 7.2248 7.231 7.2597 7.0539 7.515 7.1721 7.2139 7.1677 7.5136 7.7871 7.3071 7.2964 7.3141 7.7068 6.956 7.0733 7.0412 7.2124 7.4004667 7.7209667 7.2218333 7.1957 7.1795 7.0084667 7.1488667 7.0842 6.9981 7.0081 6.9517 7.0944 6.553 6.1891 6.2599 7.3434 7.316 7.3561 7.6635 7.4057 6.7907 7.6231667 7.6721 7.9693 8.6935667 8.6165333 7.0216 7.7450333 7.6769667 8.0958 7.9622 7.6437 8.1671 8.2917 7.8539 7.6295 7.4575 7.2295 7.2683 7.5629 7.3764 7.4898 7.2046 7.2078 7.2168 7.3672 7.1967 7.3743 7.547 7.5827 7.1684 7.4464 7.5617 7.4034 7.2117 7.447 7.2217 7.2454 7.4001 7.2652 7.2611 7.1333 7.0139333 7.3931 7.15855 7.2105 7.26085 7.2248 7.11465 7.12675 7.272 7.2605667 7.0886333 7.1487 7.1607667 7.315575 7.6231667 7.6721 7.9693 7.7450333 7.6769667 8.52934 8.6165333 7.0216 8.0958 7.9622 8.0413 7.0767 7.0479 7.4621 7.2867 7.2222 7.2895 7.7994 7.104 7.3435 7.5395 7.2736 6.8988 6.9665 7.0177 7.0846 6.949 6.7134 6.9998 7.2413 7.1132 7.0953 7.0417 7.451 7.0711 6.6153 6.5903 7.1421 7.5062 7.2271 7.077 7.2544 7.2836 6.9383 6.9103 6.9287 7.04705 6.7747 7.19955 7.11925 7.17165 6.8207 7.0478 6.8748 7.22155 6.8673 7.1177 6.94005 5.9444 7.1975 7.4994 6.3399 7.607 7.5834 7.2149333 7.40315 7.6366 7.88865 6.93925 7.377 7.9385 7.9396 7.3836 7.5626333 8.005 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8.5374 7.8648 8.6409 8.6804667 8.6313 8.5282 8.7873 8.6815333 8.6065333 8.956 8.1466333 8.2948 8.537 9.2703 8.2615667 8.1998 8.2449333 8.7042 8.1706 8.7603 8.6519 8.6015 9.0426 8.6028 8.0562 8.6212 8.5867 8.437 8.5411 8.3003 8.4566 7.819 8.2297 8.2006 8.3154 8.1293 8.0011 8.1709 8.2078 8.2991 8.1904 8.2257 7.9331 8.5188333 8.8528667 8.0317 8.47305 8.984 9.1784 8.4923 8.5712 8.3151 8.5521 7.8707 8.6116 7.7996 8.7877667 8.5859333 8.8554333 8.6776667 8.7423333 8.7623333 8.3512 8.5304 8.7861 8.6568 8.9135 7.9995 8.67065 8.2476 8.1451 8.2409 8.5652 9.0099 8.4221 8.8303 8.8134 8.5654333 8.0912 9.037 7.33075 7.06665 7.65935 8.32985 8.17775 8.17385 8.11985 8.4576333 8.3406333 8.5918667 8.0494333 8.1793 8.0560667 7.8831 8.1784 7.3795 8.2309 8.3315 8.079 8.2368 8.2917 8.6272 8.75515 8.66835 8.7122 8.7529 8.53615 8.57355 8.7917 8.6966 8.7539 8.5743 8.3059 8.6072 8.2938 8.1153 7.8804 8.3401 8.6271 8.3459 8.8986 8.4978 8.559 7.8455 7.8905 7.9957 7.8854 7.9424 7.9464 7.8284 8.048 7.7076 7.856 8.1211 8.0281 7.9047 7.7476 8.1032 8.3783 7.4881 8.0934 7.8123 7.8621 7.7337 7.916 8.2141 7.7745 7.9589 7.7635 8.3972 8.6375 8.5419 8.0848 8.3569 8.6843 8.5781 7.97864 8.3627 8.2084 7.9303 7.6447 7.8436 7.6527 7.6827 7.9991 7.8897 8.1997 8.364 8.2948 7.7793 7.9323 7.9849 8.2385 8.0615 8.689 8.2625 8.4384 8.3636 7.9061 7.9195 8.2723 7.9899 8.052 8.0732 7.767 8.0226 8.0803 8.0509 7.8722 8.4048 8.0004 7.945 8.3058 8.147 8.1524 8.1225 8.249 8.2009 7.7867 8.3808 7.9298 7.9149 7.9286 7.9042 8.1435 8.1405 8.4901667 7.9185333 7.7851333 7.7061 7.8462667 8.0559333 8.0033 7.8582 7.777 8.3839 8.6534 9.4432 10.458 7.9525 7.7536 6.9723 8.7596 8.2039 7.2225 8.1835 8.1352 8.9167667 8.8022667 8.7818 8.8082333 8.5515667 8.5207 8.35 8.4303333 8.581 8.4609 8.6672 8.6299 8.8417 8.372 8.0726 8.5752 7.9142 9.0259 8.8101 8.4615 8.0183 8.4307 8.5003 8.3258 8.5508 8.5386 8.6048 8.2218 8.5315 8.4559 8.6213 8.4303 8.4524 8.602 8.6441 8.7336 8.8588 8.1466 8.2007 8.2162 8.14255 8.2483 8.1605 8.288 8.1535333 8.16045 8.1596 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11.169 11.475 11.631 12.007 10.945 11.3325 12.208 12.39 12.078 8.1610667 11.974 12.097 11.886 11.913 11.881 12.059 11.697 12.362 12.275 11.62 12.141 11.737 11.412 11.168 10.544 11.1366667 11.5646667 10.7903333 11.51 12.79625 11.126 11.575 12.061 11.9 11.987 11.798 12.307 12.631 11.914 12.5793333 12.5506667 12.683 12.5926667 12.5526667 12.5606667 11.4233333 11.409 12.166 11.904 12.59 11.872 11.805 12.021 9.7223 12.317 12.01 11.639 12.601 12.829 11.37 12.0196667 10.005 9.076 13.1315 12.863 12.4055 12.206 11.6575 11.72 11.6745 12.5106667 12.787 12.2656667 12.6496667 12.6213333 12.5113333 10.442 11.3 9.8655 11.576 11.727 8.61 9.9974 12.6635 12.296 11.9205 11.6965 11.6565 11.499 12.9655 12.509 11.611 11.5585 11.6095 11.675 11.777 11.47 11.53 8.5973 9.7558 8.5014 10.242 12.027 11.815 11.785 11.132 12.202 11.694 12.246 11.996 12.347 12.174 12.039 11.733 12.467 12.202 12.397 12.498 12.189 12.005 12.513 12.458 11.787 11.678 11.358 11.541 11.439 10.119 9.6005 9.9519 10.083 10.015 11.867 11.932 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12.22 11.777 10.264 7.328 9.9267 11.903 9.9385333 11.0145 8.7908 11.8405 9.94 11.4536667 10.46 7.5869 10.239 11.124 11.3804 10.341 10.9878 11.3405 9.8294 7.168 11.349 10.7923333 10.106 11.6636667 9.27345 9.49175 11.5216 6.9202 9.2425 9.9584 7.2475 10.364 8.9269667 9.7448 11.2633333 9.5451 9.9854 10.6516667 8.0444 10.6865 10.74325 7.5831333 7.8136 10.661 10.414 8.6322 10.6086667 9.5203 9.6282667 9.7448 9.64 9.8044 9.7928333 9.9847667 8.0444 9.1955 7.0216 10.094 11.4975 11.7955 11.208 11.8142 11.9902 10.421 9.68648 10.5346 8.86035 9.9003 7.4641 10.685 10.712 10.9226667 10.7366667 10.936 10.6866667 10.8693333 10.057 11.99 7.854 6.959 9.46985 11.692 11.442 11.9363333 11.517 8.4327 9.4801 10.022 9.062 10.283 9.7325 10.6 10.036 6.6233 10.065 10.039 10.335 9.8972 10.696 9.686 9.636 10.343 9.9077 11.673 10.718 11.475 10.637 9.4861 8.6538 9.4854 11.5553333 10.5273333 8.7335 10.826 10.942 10.66 10.721 11.011 11.255 10.978 7.3968 10.816 12.312 11.013 10.54 10.3433333 10.5 10.91 10.8363333 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8.4744 7.9951 8.7385 8.8426 8.7844 8.6539 8.7549 8.9581 8.8844 8.7935 8.7466 9.167 9.1157 9.0411 8.9957 8.3438 8.5379 8.3771 8.7925 9.1788 9.6112 9.5551 8.9133 8.7456 9.5751 8.8324 8.7533 8.74108 8.77278 8.83558 8.56304 8.9684 9.064 9.0524 8.9159 9.0902 8.9092 9.1389 8.645 9.6326 8.9428 8.9042 9.2002 9.148 8.9417 8.7255 8.8116 8.4962 9.1316 9.0947 9.2265 9.3342 9.3599 9.0795 8.7614 8.8649 8.8549 9.1879 8.7478 9.1303 9.3052 9.2776 9.0864 9.4297 9.1347 9.299 9.2552 9.2825 9.246 9.1264 8.9043 8.9378 8.9351 8.794 8.701 8.86 8.6254667 9.2019 7.7544667 7.6268 7.6883667 7.8084333 7.7316333 9.2359 9.0141 9.3867 10.529 8.8115 8.8839 9.0343 8.6675 8.8769 9.3603 8.3116 8.6158 9.0756 8.3568667 8.3037333 8.5193333 8.4759 9.0327667 8.3089333 8.4378667 8.3997667 8.4772 8.5767 9.0474 8.3367 8.3346 9.1446 9.0371 9.1822 8.9597 8.8619 8.8717 8.3846 8.739 9.5256 9.1633 9.3271 9.2336 9.3447 9.3678 9.2594 9.3373 9.3391 9.114 9.1358 9.2761 9.1086 9.0035 8.7705 8.3413 8.565 8.2627 8.9524 8.8351 8.9221667 9.0861 8.6314 8.6907 9.01555 8.8554333 8.7352 8.7869 8.9193 8.9883333 8.8860333 8.9124667 8.9784667 9.0925 8.3568667 8.3037333 8.5193333 8.4378667 8.3997667 8.41266 9.0327667 8.3089333 8.4772 8.5767 8.8243 9.4878 9.1153 8.2415 8.8195 9.0977 8.4207 8.893 8.7225 8.6889 8.4733 8.3575 8.7169 9.0537 8.8042 8.6524 8.8206 8.4029 9.1204 9.0441 8.8832 9.1354 7.9256 7.8842 8.1252 8.5817 8.7859 9.2282 9.3627 9.4743 9.2911 9.1786 9.3794 8.6362 8.8737 8.618 8.95715 8.9671 9.0836 9.2046 9.0805 8.75285 9.13045 8.88535 9.04065 9.0085 9.2919 9.06575 8.4806 9.2498 9.0284 8.8013 8.9034 8.1272 8.1396667 7.92555 7.92845 8.0551 7.4466 8.4062 8.1619 8.1645 8.3411667 8.6397 8.5805667 8.5525333 8.6443333 8.6362 8.7909 8.7189333 8.9854 9.0263 9.2641 8.4182 8.3867 9.042 9.0504 9.1118 8.2692 7.8908 8.8897 8.8646 8.9001 8.9746 8.6279 8.9248 8.7119 8.8122 8.5739 8.4825 8.7893 8.7008 8.585 7.8353 8.2462 8.6924 8.6692 8.5443 8.9076 8.6033 8.8596 8.8066 8.9392 8.8331 8.9096 8.9555 8.77955 8.62235 8.7012 8.7013 8.7977 7.9395 7.9226 8.6405 7.8247 8.6309 9.1695 9.3834 +"G4209" 9.954 9.5257 9.53995 10.129 7.72335 10.495 10.429 10.5595 10.838 10.03695 9.7582 9.5719 8.04015 9.5683 9.3259 9.5514 11.9136667 7.9266 11.614 10.123 11.134 10.7023333 8.26695 11.822 10.797 7.7529333 10.14036 10.692 10.2306 7.89625 11.953 10.15 10.251 10.2676667 7.91 9.3546333 12.1365 9.6474 8.00082 11.452 11.926 10.243 10.171 9.8823 10.5416667 12.453 8.3165667 9.4139 10.108 9.5834667 12.169 9.94405 10.1308 9.6305 11.183 8.2135667 9.53265 10.335 12.4596667 9.4792 8.4082333 12.453 8.6411 9.6619 8.794 9.3481333 12.169 10.417 10.255 11.278 7.8501 8.04145 7.5826333 10.1816 10.04874 10.671 10.6206 11.0938 8.5176 11.546 10.507 10.315 10.3583333 10.1208 10.3616667 10.449 10.5873333 10.151 7.9679 9.5812 12.083 12.115 9.558 8.9053333 8.2164 9.1956667 8.0987 11.365 12.346 12.601 11.353 11.6785 10.253 9.9915 10.249 8.5789667 11.03 10.128 10.691 10.2 10.494 10.611 10.311 10.267 10.269 10.286 11.092 10.782 10.374 11.201 9.6594667 9.5646 8.2415 7.8072333 11.373 7.9504 9.3612 9.0132 10.638 8.236 7.5764 8.5742 11.856 10.058 9.6015 10.1326667 10.2343333 10.4076667 10.7416667 10.2023333 10.0783333 9.0518667 11.811 7.9732667 10.392 9.5168 11.24 10.722 10.271 11.998 8.8047 7.5011 7.9141 10.34 10.702 10.235 8.4488 11.392 8.2963 9.6391 9.77095 10.928 9.40545 10.982 10.644 10.2235 9.2963 9.0457333 9.2230333 8.2741667 9.8102333 9.1040333 11.503 11.801 12.4285 10.848 10.588 10.043 12.045 10.1175 12.021 12.159 12.502 12.4145 12.7205 10.241 11.215 12.221 12.4245 12.587 11.248 11.682 11.414 11.579 10.003 11.765 10.087 11.916 11.101 11.963 11.928 10.917 11.385 11.61 11.591 11.832 11.28 11.004 10.904 11.081 11.265 11.553 11.656 11.744 11.501 11.801 11.992 11.872 10.14 9.9642 9.679 10.321 10.652 10.788 10.384 11.032 10.872 10.828 10.818 11.069 11.31 12.346 11.083 12.168 11.216 10.3566 10.2938 10.4998 10.3888 10.659 10.894 10.562 10.258 10.29 10.717 11.349 11.085 11.637 10.499 10.564 10.485 10.876 10.681 11.456 11.017 10.915 10.053 10.366 10.119 10.335 10.581 10.237 10.35 9.4334 9.426 10.015 9.298 10.354 10.73 10.404 10.113 10.114 10.465 10.864 10.8 9.9554 10.606 10.803 10.417 9.8537 9.981 10.26 10.451 10.6626667 11.0943333 10.6963333 8.3224333 8.2627333 8.3257667 8.3818333 8.7123 10.203 11.348 11.406 11.454 11.528 11.536 11.392 9.5683 11.452 11.832 9.6485 12.045 10.601 9.9994667 9.1507 9.1329 10.9133333 11.2616667 9.3875667 9.3497667 8.5450667 8.6866333 8.5999667 10.532 9.9924 10.091 11.137 11.393 11.557 11.603 11.725 11.857 11.785 11.989 10.19 10.384 10.416 10.669 10.265 11.282 11.263 11.614 11.511 11.491 11.862 11.037 10.919 10.776 11.502 11.669 11.662 11.527 9.7481333 9.5696 9.5291667 8.9767 9.37645 9.5473 9.36315 8.7258667 9.5282 9.4414 9.40095 8.9608667 9.8478 9.6536 9.5068667 9.276875 9.9994667 9.1507 9.1329 9.3497667 8.5450667 10.872 11.2616667 9.3875667 8.6866333 8.5999667 10.763 10.826 10.169 10.781 11.157 10.305 11.098 9.4563 11.735 11.576 11.473 11.5 10.39 9.9717 10.126 11.33 9.7198 10.506 11.249 10.529 11.728 11.712 8.4051 8.3305 8.4843 11.355 11.307 11.726 9.6679 9.9104 9.8507 10.862 9.5097 11.055 10.763 11.2 10.26 11.122 10.9765 10.9735 10.1415 11.0295 10.797 10.489 10.219 11.131 11.023 11.0755 10.267 11.183 12.169 11.084 11.638 11.904 8.0398 7.96305 8.1788 9.9345 9.31165 8.195 8.87795 9.5568 10.8906667 10.525 11.2413333 12.294 11.0306667 11.335 11.8276667 12.259 10.754 10.645 10.709 11.881 12.147 10.485 10.027 9.4391 9.6466 10.127 10.271 10.598 10.82 10.839 11.606 10.84 10.669 10.757 11.158 11.296 10.88 10.403 10.152 10.038 9.8749 10.678 10.523 10.715 10.357 10.323 10.244 9.7181 10.298 10.483 10.556 10.015 9.5309 9.01715 8.8851 8.8533 8.7019 11.205 11.674 9.0548 10.439 11.016 9.7893 10.419 +"G4210" 7.9336 8.7720333 8.5008 8.45575 8.4013 8.6764 8.8208333 8.1011 8.6311333 8.15905 8.4626 8.2875 8.2994 9.2535 8.0151 8.5853 8.3319667 8.3139 8.6735 8.2147 8.1039 8.0880667 8.2879 9.2108 7.6984 8.4589667 8.39344 8.3777 8.16886 8.06555 8.3094 8.8277 8.4504 8.0532 8.2119 8.8969667 8.3923 7.84465 8.44566 8.7427 8.35095 8.252 8.8684 8.1641 8.6353 8.3597 8.4204 8.1279 8.3591 8.2259333 9.2028 8.2194 7.862475 9.0666333 8.0395 8.3326 8.2506 8.6001 8.0744 8.2693 8.6576667 8.3597 8.2649 9.1263 8.6009333 8.1226667 9.2028 8.4699 9.4953 8.3317 8.45915 8.5867 8.3289 8.5308 8.33856 8.308 8.3029 8.26124 8.82065 8.4455 9.2321 8.5213 8.1324667 8.1540667 7.9719 8.1884667 8.0372333 8.1858667 8.2762 8.4832333 8.66 9.2767 8.081 8.2853333 8.2272 8.5912667 8.4802 8.565 8.5018 8.0142 8.379 8.1739 8.309 7.9094 8.3113 8.7911333 8.1839 8.1567 8.1777 8.1609 8.5006 8.1488 8.6316 8.2121 8.3614 8.1484 8.2934 8.2861 7.8409 8.5131 8.4278 8.8686667 8.467 8.1389333 8.546 8.433125 8.4268 8.327 8.1326 8.2625 8.3152 8.2728 8.975 8.2816 8.39135 7.8453667 8.0938 7.8380667 8.0559333 7.8772667 7.9989 8.8431667 9.0356 8.0104667 8.298 8.4651 8.4315 8.24315 8.3336 8.2331 8.4689 8.354 8.4832 8.1427 7.5196 8.3317 7.8974667 8.9393 8.1975 8.7972 8.9142 8.7356 8.3617 8.526 8.3341 8.00165 8.1885 8.1582667 8.1394333 8.1069667 8.0096667 8.1309333 8.7363 9.157 8.87185 8.5274 8.3409 8.7995 8.741 8.0692 8.0871 8.0849 8.10935 8.1458 8.13075 8.0853 8.0657 8.15195 7.979 8.14515 8.3075 8.3722 8.4394 8.4754 9.1403 9.3435 9.4036 8.4608 8.2596 8.0977 8.726 8.7475 8.1001 8.1854 8.2138 8.2485 8.0642 8.229 8.024 8.1388 8.0709 8.1474 8.3942 8.112 8.1894 8.1983 8.2711 8.1986 7.9626 8.1616 8.241 8.116 7.8567 8.3342 8.374 8.2979 8.4535 7.9001 8.5044 8.0554 8.0016 7.9479 8.0388 7.8471 8.2 8.2681 8.41024 8.45064 8.43082 8.3908 8.4727 8.181 8.4926 8.4008 8.562 8.5578 8.4448 8.213 8.2803 8.7217 8.3958 8.3994 8.5427 8.6875 8.5613 8.65 8.5953 8.2374 8.1261 8.3283 8.2015 8.2968 8.2874 8.1768 8.2083 8.294 8.2998 8.1054 8.0914 8.178 8.1649 8.0955 8.1409 8.1876 8.5906 8.33 8.0904 8.2943 8.3651 8.1669 8.0894 8.2563 8.3125 8.19 8.3564 8.2641 8.3620333 8.1673667 8.179 8.1697333 8.0063667 7.8665 8.0927 8.1572 8.3775 8.3167 8.2714 8.6779 9.2535 8.7427 8.7036 8.8787 8.5617 9.9522 8.6664 8.8584 8.6873667 8.3003 8.2047667 8.8933667 8.9693333 8.8289333 9.4122667 9.5818333 8.2725 8.4931 8.8928 8.2237 7.7969 7.9736 7.9193 8.1981 8.071 8.1164 8.2611 8.1908 8.1287 8.347 8.2023 8.2811 8.1754 8.0362 8.1359 9.545 9.0778 8.953 8.5864 8.2349 8.4553 8.2688 8.2372 8.2369 8.0929 8.0841 8.1299 8.1428 8.2736 8.21185 8.20185 8.2419 8.2964 8.3075 8.232 8.1696 8.2221333 8.2010667 8.1172333 8.1454333 8.180075 8.6664 8.8584 8.6873667 8.9693333 8.8289333 8.29672 8.2047667 8.8933667 9.4122667 9.5818333 9.5045 10.813 8.4109 8.766 9.2941 8.8317 8.3888 9.1936 8.1999 8.3456 8.253 8.1046 8.4397 8.3687 8.695 8.4136 8.7502 8.0007 8.3677 8.6489 8.3447 8.0856 8.1732 8.7075 8.2827 8.3475 8.1135 8.4612 8.1139 8.1884 8.2315 8.1331 8.1635 8.3978 8.0643 8.0972 8.0763 7.98365 7.98655 7.9263 8.2313 7.92615 7.9958 7.7516 8.10005 8.0703 7.95625 7.67305 8.7417 8.0395 9.2028 8.1835 8.465 8.4038 8.5071667 8.56715 8.409 8.48425 8.5435 8.4450333 8.17695 8.7731 8.2491667 8.1789667 8.2735667 8.1530333 8.2685 8.2776667 8.2189 8.3585333 8.349 8.3737 8.0909 8.1722 7.9333 8.1571 8.1209 8.0741 8.2434 8.3213 8.1932 8.1896 8.0584 8.1468 8.1369 8.1076 7.9252 8.0835 8.3024 8.2337 8.2696 8.3711 8.213 8.0506 8.3477 8.3219 8.1776 8.3574 8.2675 8.3073 8.3701 8.5247 8.1326 8.3403 8.50425 8.2864 8.3813 8.2637 8.4099 8.3821 8.3066 8.5119 8.1881 8.4539 8.2921 7.8968 8.2372 8.1677 +"G4211" 5.7667 5.5633333 5.74035 6.0422 5.64985 6.7049 5.5309333 5.4864 5.4573333 5.33155 5.9391 6.8155 5.5295 7.0054 5.9607 5.4069 5.6061 5.72505 5.4239 5.37875 5.8531 5.7131667 5.81195 6.2036 5.5973 5.7928333 5.7472 5.5123 5.4636 5.7019 5.5541 7.2434 5.4286 5.522 5.7554 5.5553 5.9088 5.38715 5.64966 7.5378 5.66815 5.3571 6.3481667 5.4261 5.9924667 5.955 5.6841 5.7741 5.4874 5.5212333 6.0764 5.23265 5.558825 5.9068 6.1056 5.2977 5.4755 5.2345 6.1621667 5.2235 5.9521667 5.955 6.0524 5.56865 5.4199333 5.6119333 6.0764 5.6323 7.1879 5.4395 5.61595 5.39685 5.8025667 5.64608 5.35588 5.2829 5.86688 5.38638 6.9066 5.3089 6.2387 5.4924 5.3164667 5.3655667 5.3025333 5.3465667 5.3677333 5.3488667 5.8962 5.4489 6.6646 7.1267 5.64855 5.3742667 5.1368 5.6664 5.4095 6.2389 5.8531 5.7182 5.5501 5.8326 5.345 5.3139 5.511 6.5489333 5.087 5.4641 5.4038 5.1758 5.4893 5.2978 5.1575 5.338 5.0382 5.3873 5.5674 5.2749 4.9937 5.5099 5.6374 5.3618333 5.7733333 5.8816333 6.4377 5.665275 5.4263 5.786 5.2531 5.6219 5.4184 5.3153 6.4777 5.4626 5.66265 5.4762 5.3875333 5.2609333 5.4030667 5.4357 5.2813333 5.9165333 5.917 5.6780333 5.424 5.4548 5.6074 5.67555 5.6447 5.5918 5.1807 5.4183 5.457 5.4407 5.3924 5.5836 5.4627333 6.3188 5.9164 5.57635 5.2909 5.0505 5.76945 5.3018 5.4995 5.661 5.1179333 5.3439667 5.2730333 5.6613333 5.457 5.6711667 6.4565 6.3065 6.30545 5.7533 5.6738 5.3699 5.1978 5.7403 5.50245 5.74725 5.62495 5.6173 6.0958 5.95055 5.9362 6.0218 6.001 6.03795 5.466 5.7999 6.2418 6.413 5.4407 5.4552 5.2836 5.2327 5.5634 5.9017 6.3551 6.3132 5.6658 5.3316 5.6124 5.6131 5.8648 5.2905 5.5501 5.3448 5.8409 5.3542 5.7 5.4511 5.6309 5.6269 5.1921 5.7715 5.3789 5.5241 5.3691 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8.75995 8.87365 8.2231333 8.5817 8.3761 8.2378 8.1869 8.1368 8.3791 7.9549667 9.2642 8.4874333 8.4786667 9.4747 9.7626 8.5812 8.26 8.1468 9.2383 8.7522 8.4316 8.6695 8.5233 10.053 11.01 10.087 8.368 8.3768 8.8855 10.219 8.7279 8.6226 8.3433 8.7738 8.638 8.3233 8.5966 8.1706 8.2362 7.9807 8.6319 8.631 8.4162 8.0081 8.8596 8.0846 7.8595 7.99 8.85485 8.12915 8.0743 7.9254 7.8278 7.6579 7.8473 7.545 7.8284 8.1441 7.3766 7.8524 7.4888 +"G4271" 9.2232 10.7223333 4.8077 9.9868 5.59255 13.672 8.7889333 9.9571 12.5763333 11.6005 5.1145 7.3476 10.0365 13.771 12.366 5.08995 10.3173333 5.51305 8.9998 5.2439 13.961 12.7186667 7.10165 8.5505 7.5823 4.9478333 4.97802 4.8982 11.0954 11.7105 5.8304 7.1777 5.2787 10.5043333 5.9446 5.0712 12.8355 4.9711 5.83846 13.593 12.008 4.6369 12.8796667 4.2166 13.1013333 13.676 7.8476 10.814 7.7932 7.9116 13.474 5.9461 11.94125 11.4336667 10.997 11.6736667 4.78045 11.482 5.5678333 5.4505 10.9158667 13.676 6.0765 7.89395 5.9413667 6.1711 13.474 12.25 7.2184 6.4995 6.0224 5.62245 5.5163667 5.4152 5.04242 5.1639 13.9984 13.3034 12.138 6.263 12.942 5.0437 11.564 9.8888667 10.794 10.85 11.629 10.6366667 8.56 4.4337333 13.989 12.041 5.42705 11.934 5.4715 13.2956667 5.3391 12.634 13.822 13.836 5.3101 11.9445 4.8786 5.085 5.1396 5.9913333 8.2401 5.8641 4.8585 5.3515 4.8535 4.9856 4.8249 8.159 7.619 4.8583 6.1402 4.9523 5.0107 5.2465 7.2919667 12.3746667 6.5631 9.3232333 11.275 12.517 6.4725 6.1007 11.94 8.4127 6.4962 5.6739 12.376 5.6749 5.14765 8.8183333 9.8840333 13.0856667 12.599 11.8526667 9.0100667 5.7035333 13.407 8.4656 4.6798 4.6017 6.6815 5.3687 12.811 4.8864 13.321 10.143 8.0182 6.2317 10.048 4.7239 12.3983333 10.392 10.882 8.64855 8.34205 8.48115 8.36715 7.4839 7.69395 7.7634 5.9954333 6.3318667 5.5930667 6.5148333 6.2943667 7.0153667 11.491 13.426 11.254 11.803 11.684 5.3183 5.4568 7.26215 13.9635 13.8845 13.879 13.7815 13.464 8.4478 12.31 13.936 14.085 14.052 13.984 14 14.096 11.417 5.5982 6.3715 5.8582 5.3286 13.401 14.15 14.12 11.302 4.6101 4.8243 4.8674 4.7167 4.7804 5.1036 4.7457 5.0497 4.8183 5.4881 6.4072 8.6011 4.3099 5.1564 6.1212 7.1666 4.1139 4.5313 4.5717 5.2124 4.6309 4.3718 4.615 4.9288 6.1443 7.1753 4.8391 5.3327 10.492 13.034 5.3325 13.689 10.388 5.09448 5.0662 5.73452 6.63832 4.6519 4.7737 4.7782 5.1456 5.3278 7.3264 10.403 11.596 6.8251 4.6843 4.9799 5.0438 7.0158 9.4534 11.593 11.827 10.954 8.6207 4.7725 5.0475 4.9797 5.1078 4.5776 4.844 4.4088 4.7806 4.7204 4.7655 4.9072 4.7267 4.9306 4.5164 4.8816 4.8112 5.2522 6.8739 4.7563 4.6051 4.917 7.9917 4.7491 5.0365 4.6661 4.8458 5.9477667 7.0474667 5.0704 5.1568333 5.1770667 5.2705667 5.2594 5.1917333 4.6785 4.3319 5.5299 6.3863 4.8807 7.2861 8.1208 13.771 13.593 11.882 8.4847 12.367 12.935 13.9686667 14.023 13.383 14.035 13.943 11.626 13.9953333 13.4756667 12.46 11.025 4.5873 4.8362 5.04 4.5177 4.632 5.101 5.1851 4.7993 4.3347 4.833 4.5128 4.8483 4.7821 4.4883 4.6334 4.6867 9.0708 8.1721 6.4928 5.5882 5.3897 5.8797 4.8087 4.4344 4.921 4.7014 4.569 4.6934 4.8309 4.6373667 4.98315 4.7935 4.9615 5.004 4.6612 4.73525 4.9512667 4.7674 4.6529 5.08445 5.0749 4.7362333 4.6658333 5.0498333 5.006475 13.9686667 14.023 13.383 13.9953333 13.4756667 13.9932 13.943 11.626 12.46 11.025 12.211 11.104 6.9548 6.8904 6.6075 5.1423 5.6218 5.773 4.5741 5.1857 5.1115 6.4183 4.7025 5.825 5.8662 10.147 5.9049 5.5888 5.0724 4.7681 5.3635 5.2877 6.0333 6.2964 6.0931 5.476 6.055 5.5679 4.7272 4.6314 4.622 4.7227 4.741 4.8267 4.4765 4.6604 4.37765 4.85275 7.11285 4.76945 4.2807 4.87595 6.4558 5.3617 4.47565 4.78995 7.2003 5.072 12.662 10.997 13.474 13.291 13.676 12.815 5.7756333 6.9266 12.991 11.7885 7.6437 5.9826667 11.456 7.7917 4.4284667 4.4259 6.5405667 8.3159333 4.1482667 4.8677 9.0413 8.3229667 5.4472 6.4276 7.495 13.988 13.719 5.3877 6.5376 7.6684 12.383 11.734 5.6495 5.2955 6.5934 7.4791 13.323 5.9009 6.0631 6.6767 7.0681 7.5923 5.7658 6.175 6.8943 8.5136 8.5575 4.6582 5.0343 6.0065 6.7553 6.661 4.9605 4.972 5.2978 6.0885 5.11585 4.8471 4.9403 5.85155 7.1069 5.9653 7.3119 5.072 5.8831 6.4359 4.8843 6.5733 4.817 4.8644 +"G4272" 12.6345 12.295 12.9895 8.6047 11.371 10.911 12.621 12.13 12.2716667 12.038 12.955 12.174 10.452 7.705 10.987 12.644 8.6527333 11.4625 8.5067 12.589 10.531 11.9523333 10.835 7.28 11.004 11.4 11.7148 10.773 11.4952 11.4355 8.8699 5.9904 12.087 12.1703333 12.198 13.157 9.451 10.8735 11.6408 6.5328 10.5546 11.625 8.5767667 10.8 9.1847667 9.9582 11.0803333 11.191 12.936 11.8633333 7.2928 13.0595 11.7225 12.3266667 8.6087 12.1353333 12.8085 8.2939 11.7793333 12.155 10.949 9.9582 9.5376 10.627 9.4343667 10.9316667 7.2928 10.135 6.3765 10.565 11.294 11.2795 11.0233333 12.2388 12.4206 10.799 9.74964 11.4174 8.6051 9.2002 5.9623 11.724 12.2956667 12.3163333 12.021 12.0073333 12.064 12.324 9.0199 13.076 7.6673 6.7414 10.615 11.211 11.475 10.8793333 11.392 7.2204 10.185 10.621 10.867 11.475 11.247 11.807 11.69 6.8272 11.733 10.64 12.278 10.919 12.046 10.8 10.643 10.865 10.951 12.212 11.481 12.332 12.313 10.867 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8.7402 9.6470667 10.5565 11.534 9.2208 9.8933 10.09805 12.0665 9.2004667 9.6381 10.627 12.1135 9.2172 9.3998 10.3469 11.6203333 9.30845 9.7606 9.648 10.4023333 9.8529333 10.173 10.4716 10.0531 8.9649667 8.5465 8.4906 9.7081 8.899 8.9057 8.6009 8.2678 8.4437 9.2686 7.8836 9.7271 9.4757 8.8516 9.0519 10.044 9.3082 8.8393 9.6353 9.0837 8.9887 9.5673 9.8859 8.822 8.4908 11.646 12.158 12.037 9.8411 9.9575 9.8387 10.591 8.8862 8.6974 8.9921 8.7111 9.3271 9.4687 9.7709 10.772 9.44385 9.553 9.638 11.21 9.4586 9.3302 10.008 11.2705 9.58985 9.5211 9.56925 11.193 10.16 10.022 10.933 10.66 10.863 8.1040333 8.4911 9.5738 9.0936 9.34035 8.1496667 9.4449 9.2989 9.4703 9.4621667 10.2287667 10.7923333 8.8751 10.2852 9.7978333 10.564 11.543 9.7936 9.7498 11.081 10.558 10.548 10.075 10.197 11.536 10.651 9.2667 9.403 9.7894 9.9541 11.496 10.125 9.7244 9.5091 9.6394 10.363 9.5967 9.7714 10.044 10.34 10.031 9.5447 9.4061 9.8406 9.7297 9.4441 9.0298 9.0717 8.8328 9.3529 8.92625 9.46145 9.3615 9.73795 8.5791 8.7144 9.8911 8.1483 8.1563 8.3105 8.3175 7.2666 8.1019 7.8373 +"G4423" 10.918 11.6553333 13.51 9.11635 10.326 10.07 11.898 9.74945 11.89 10.2415 12.667 6.9898 9.7781 8.7673 10.248 10.7585 8.6402 10.762 9.048 10.5195 9.98695 11.1326667 9.96875 8.4187 10.928 11.712 11.1344 10.753 11.3244 10.1415 9.9615 8.7415 11.095 10.5873333 9.6787 10.491 9.0402 10.5735 10.6454 8.2698 9.9589 10.366 8.5473333 9.7941 8.6827333 8.7696 10.5893333 9.2292 9.9409 9.6948 8.1153 11.0725 10.645 9.5294333 8.9063 10.7163333 10.9425 8.899 8.1279 10.823 9.6611 8.7696 9.2066 10.3555 9.8578667 10.4756667 8.1153 9.8002 8.6316 10.794 10.2755 10.286 11.7083333 11.0318 11.3446 10.764 11.5478 11.025 9.62795 10.159 8.4847 10.766 10.492 10.6173333 10.5173333 10.5726667 10.498 10.4086667 9.2578 10.5383333 8.5118 8.1775 10.42 10.3696667 10.542 10.216 10.454 8.7548 9.5002 9.4853 10.683 10.157 10.436 10.479 10.417 8.4354 10.463 9.9842 10.833 10.155 10.973 10.652 9.8054 9.9403 9.808 10.604 11.006 10.58 10.478 10.928 10.0555667 9.8009333 10.6363333 10.2324333 8.629 11.0375 10.529 10.561 10.15 9.7298 9.9293 10.689 8.3886 10.845 13.501 10.725 10.3533333 10.3886667 9.8538667 10.664 10.748 9.5353667 8.4243 10.5257667 11.227 10.805 11.312 11.685 8.9543 9.0439 9.0176 9.2367 9.8069 11.642 11.432 11.212 10.673 8.5195 9.05 10.5695 10.486 10.2405 9.99845 10.461 10.1825 10.068 9.6084 10.0267 9.5996667 10.1536333 10.2308333 9.9338667 8.7208 8.4464 8.81045 10.459 9.3404 9.0534 9.5423 10.9495 9.72085 9.29465 9.1444 9.0129 9.05965 11.2755 10.332 9.4937 9.71505 9.95945 9.2236 9.2146 8.7389 8.551 8.8873 9.3159 8.6424 9.8247 9.7654 9.2249 8.8579 8.7032 10.92 10.894 10.721 11.011 10.93 10.629 10.451 10.171 10.761 10.602 10.869 10.533 10.896 10.817 9.4625 10.822 10.38 10.499 10.771 10.756 10.21 9.2492 8.8203 8.7649 8.5652 8.6069 10.989 11.051 9.8391 9.7398 10.993 9.5298 9.7724 11.832 11.6804 11.4562 11.8004 10.411 10.455 10.521 10.589 10.84 10.737 10.943 10.544 10.746 10.363 10.448 10.765 11.082 10.864 10.578 10.465 10.487 9.977 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"TS_201_248_t0" +TRUE "m" "TS_201_248_t0" 60 "TS_202_249_t60" +TRUE "m" "TS_202_249_t60" 60 "TS_203_250_t120" +TRUE "l" "TS_203_250_t120" 120 "TS_204_251_t240" +TRUE "f" NA 0 "TS_205_252_t0" +TRUE "m" "TS_205_252_t0" 60 "TS_206_253_t60" +TRUE "m" "TS_206_253_t60" 60 "TS_207_254_t120" +TRUE "l" "TS_207_254_t120" 120 "TS_208_255_t240" +TRUE "f" NA 0 "TS_209_256_t60" +TRUE "m" "TS_209_256_t60" 60 "TS_210_257_t120" +TRUE "l" "TS_210_257_t120" 120 "TS_211_258_t240" +TRUE "f" NA 0 "TS_212_259_t60" +TRUE "m" "TS_212_259_t60" 60 "TS_213_260_t120" +TRUE "l" "TS_213_260_t120" 120 "TS_214_261_t240" +TRUE "f" NA 0 "TS_215_262_t60" +TRUE "m" "TS_215_262_t60" 60 "TS_216_263_t120" +TRUE "l" "TS_216_263_t120" 120 "TS_217_264_t240" +TRUE "f" NA 0 "TS_218_265_t60" +TRUE "m" "TS_218_265_t60" 60 "TS_219_266_t120" +TRUE "l" "TS_219_266_t120" 120 "TS_220_267_t240" +TRUE "f" NA 0 "TS_221_268_t0" +TRUE "m" "TS_221_268_t0" 20 "TS_222_269_t20" +TRUE "m" "TS_222_269_t20" 0 "TS_223_270_t20" +TRUE "m" "TS_223_270_t20" 0 "TS_224_271_t20" +TRUE "m" "TS_224_271_t20" 0 "TS_225_272_t20" +TRUE "m" "TS_225_272_t20" 0 "TS_226_273_t20" +TRUE "m" "TS_226_273_t20" 0 "TS_227_274_t20" +TRUE "l" "TS_227_274_t20" 0 "TS_228_275_t20" +TRUE "f" NA 0 "TS_229_276_t0" +TRUE "m" "TS_229_276_t0" 120 "TS_230_277_t120" +TRUE "m" "TS_230_277_t120" 100 "TS_231_278_t220" +TRUE "l" "TS_231_278_t220" 140 "TS_232_279_t360" +TRUE "f" NA 0 "TS_233_280_t0" +TRUE "m" "TS_233_280_t0" 120 "TS_234_281_t120" +TRUE "m" "TS_234_281_t120" 100 "TS_235_282_t220" +TRUE "l" "TS_235_282_t220" 140 "TS_236_283_t360" +TRUE "f" NA 0 "TS_237_284_t0" +TRUE "m" "TS_237_284_t0" 120 "TS_238_285_t120" +TRUE "m" "TS_238_285_t120" 100 "TS_239_286_t220" +TRUE "l" "TS_239_286_t220" 140 "TS_240_287_t360" +TRUE "f" NA 0 "TS_241_395_t480" +TRUE "m" "TS_241_395_t480" 1320 "TS_242_396_t1800" +TRUE "l" "TS_242_396_t1800" 1080 "TS_243_397_t2880" +TRUE "f" NA 0 "TS_244_398_t480" +TRUE "m" "TS_244_398_t480" 1320 "TS_245_399_t1800" +TRUE "l" "TS_245_399_t1800" 1080 "TS_246_400_t2880" +TRUE "f" NA 0 "TS_247_401_t180" +TRUE "m" "TS_247_401_t180" 90 "TS_248_402_t270" +TRUE "m" "TS_248_402_t270" 390 "TS_249_403_t660" +TRUE "m" "TS_249_403_t660" 300 "TS_250_404_t960" +TRUE "l" "TS_250_404_t960" 480 "TS_251_405_t1440" +TRUE "f" NA 0 "TS_252_406_t0" +TRUE "m" "TS_252_406_t0" 480 "TS_253_407_t480" +TRUE "l" "TS_253_407_t480" 480 "TS_254_408_t960" +TRUE "f" NA 0 "TS_255_409_t180" +TRUE "m" "TS_255_409_t180" 120 "TS_256_410_t300" +TRUE "m" "TS_256_410_t300" 180 "TS_257_411_t480" +TRUE "l" "TS_257_411_t480" 240 "TS_258_412_t720" +TRUE "f" NA 0 "TS_259_413_t180" +TRUE "m" "TS_259_413_t180" 120 "TS_260_414_t300" +TRUE "m" "TS_260_414_t300" 180 "TS_261_415_t480" +TRUE "l" "TS_261_415_t480" 240 "TS_262_416_t720" +TRUE "f" NA 0 "TS_263_440_t0" +TRUE "m" "TS_263_440_t0" 60 "TS_264_441_t60" +TRUE "m" "TS_264_441_t60" 60 "TS_265_442_t120" +TRUE "m" "TS_265_442_t120" 120 "TS_266_443_t240" +TRUE "l" "TS_266_443_t240" 120 "TS_267_444_t360" +TRUE "f" NA 0 "TS_268_445_t0" +TRUE "m" "TS_268_445_t0" 60 "TS_269_446_t60" +TRUE "m" "TS_269_446_t60" 60 "TS_270_447_t120" +TRUE "m" "TS_270_447_t120" 120 "TS_271_448_t240" +TRUE "l" "TS_271_448_t240" 120 "TS_272_449_t360" +TRUE "f" NA 0 "TS_273_450_t0" +TRUE "m" "TS_273_450_t0" 60 "TS_274_451_t60" +TRUE "m" "TS_274_451_t60" 60 "TS_275_452_t120" +TRUE "m" "TS_275_452_t120" 120 "TS_276_453_t240" +TRUE "l" "TS_276_453_t240" 120 "TS_277_454_t360" +TRUE "f" NA 0 "TS_278_455_t0" +TRUE "m" "TS_278_455_t0" 60 "TS_279_456_t60" +TRUE "m" "TS_279_456_t60" 60 "TS_280_457_t120" +TRUE "m" "TS_280_457_t120" 120 "TS_281_458_t240" +TRUE "l" "TS_281_458_t240" 120 "TS_282_459_t360" +TRUE "f" NA 0 "TS_283_460_t0" +TRUE "m" "TS_283_460_t0" 60 "TS_284_461_t60" +TRUE "m" "TS_284_461_t60" 60 "TS_285_462_t120" +TRUE "m" "TS_285_462_t120" 120 "TS_286_463_t240" +TRUE "l" "TS_286_463_t240" 120 "TS_287_464_t360" +TRUE "f" NA 0 "TS_288_465_t0" +TRUE "m" "TS_288_465_t0" 60 "TS_289_466_t60" +TRUE "m" "TS_289_466_t60" 60 "TS_290_467_t120" +TRUE "m" "TS_290_467_t120" 120 "TS_291_468_t240" +TRUE "l" "TS_291_468_t240" 120 "TS_292_469_t360" +TRUE "f" NA 0 "TS_293_470_t0" +TRUE "m" "TS_293_470_t0" 60 "TS_294_471_t60" +TRUE "m" "TS_294_471_t60" 60 "TS_295_472_t120" +TRUE "l" "TS_295_472_t120" 60 "TS_296_473_t180" +TRUE "f" NA 0 "TS_297_474_t0" +TRUE "m" "TS_297_474_t0" 60 "TS_298_475_t60" +TRUE "m" "TS_298_475_t60" 60 "TS_299_476_t120" +TRUE "l" "TS_299_476_t120" 60 "TS_300_477_t180" +TRUE "f" NA 0 "TS_301_478_t0" +TRUE "m" "TS_301_478_t0" 40 "TS_302_479_t40" +TRUE "l" "TS_302_479_t40" 200 "TS_303_480_t240" +TRUE "f" NA 0 "TS_304_481_t40" +TRUE "l" "TS_304_481_t40" 200 "TS_305_482_t240" +TRUE "f" NA 0 "TS_306_483_t0" +TRUE "m" "TS_306_483_t0" 40 "TS_307_484_t40" +TRUE "l" "TS_307_484_t40" 200 "TS_308_485_t240" +TRUE "f" NA 0 "TS_309_486_t40" +TRUE "l" "TS_309_486_t40" 200 "TS_310_487_t240" diff --git a/Inferelator/input/ecoli/tf_names.tsv b/Inferelator/input/ecoli/tf_names.tsv new file mode 100644 index 0000000..b39c376 --- /dev/null +++ b/Inferelator/input/ecoli/tf_names.tsv @@ -0,0 +1,334 @@ +"G1" +"G2" +"G3" +"G4" +"G5" +"G6" +"G7" +"G8" +"G9" +"G10" +"G11" +"G12" +"G13" +"G14" +"G15" +"G16" +"G17" +"G18" +"G19" +"G20" +"G21" +"G22" +"G23" +"G24" +"G25" +"G26" +"G27" +"G28" +"G29" +"G30" +"G31" +"G32" +"G33" +"G34" +"G35" +"G36" +"G37" +"G38" +"G39" +"G40" +"G41" +"G42" +"G43" +"G44" +"G45" +"G46" +"G47" +"G48" +"G49" +"G50" +"G51" +"G52" +"G53" +"G54" +"G55" +"G56" +"G57" +"G58" +"G59" +"G60" +"G61" +"G62" +"G63" +"G64" +"G65" +"G66" +"G67" +"G68" +"G69" +"G70" +"G71" +"G72" +"G73" +"G74" +"G75" +"G76" +"G77" +"G78" +"G79" +"G80" +"G81" +"G82" +"G83" +"G84" +"G85" +"G86" +"G87" +"G88" +"G89" +"G90" +"G91" +"G92" +"G93" +"G94" +"G95" +"G96" +"G97" +"G98" +"G99" +"G100" +"G101" +"G102" +"G103" +"G104" +"G105" +"G106" +"G107" +"G108" +"G109" +"G110" +"G111" +"G112" +"G113" +"G114" +"G115" +"G116" +"G117" +"G118" +"G119" +"G120" +"G121" +"G122" +"G123" +"G124" +"G125" +"G126" +"G127" +"G128" +"G129" +"G130" +"G131" +"G132" +"G133" +"G134" +"G135" +"G136" +"G137" +"G138" +"G139" +"G140" +"G141" +"G142" +"G143" +"G144" +"G145" +"G146" +"G147" +"G148" +"G149" +"G150" +"G151" +"G152" +"G153" +"G154" +"G155" +"G156" +"G157" +"G158" +"G159" +"G160" +"G161" +"G162" +"G163" +"G164" +"G165" +"G166" +"G167" +"G168" +"G169" +"G170" +"G171" +"G172" +"G173" +"G174" +"G175" +"G176" +"G177" +"G178" +"G179" +"G180" +"G181" +"G182" +"G183" +"G184" +"G185" +"G186" +"G187" +"G188" +"G189" +"G190" +"G191" +"G192" +"G193" +"G194" +"G195" +"G196" +"G197" +"G198" +"G199" +"G200" +"G201" +"G202" +"G203" +"G204" +"G205" +"G206" +"G207" +"G208" +"G209" +"G210" +"G211" +"G212" +"G213" +"G214" +"G215" +"G216" +"G217" +"G218" +"G219" +"G220" +"G221" +"G222" +"G223" +"G224" +"G225" +"G226" +"G227" +"G228" +"G229" +"G230" +"G231" +"G232" +"G233" +"G234" +"G235" +"G236" +"G237" +"G238" +"G239" +"G240" +"G241" +"G242" +"G243" +"G244" +"G245" +"G246" +"G247" +"G248" +"G249" +"G250" +"G251" +"G252" +"G253" +"G254" +"G255" +"G256" +"G257" +"G258" +"G259" +"G260" +"G261" +"G262" +"G263" +"G264" +"G265" +"G266" +"G267" +"G268" +"G269" +"G270" +"G271" +"G272" +"G273" +"G274" +"G275" +"G276" +"G277" +"G278" +"G279" +"G280" +"G281" +"G282" +"G283" +"G284" +"G285" +"G286" +"G287" +"G288" +"G289" +"G290" +"G291" +"G292" +"G293" +"G294" +"G295" +"G296" +"G297" +"G298" +"G299" +"G300" +"G301" +"G302" +"G303" +"G304" +"G305" +"G306" +"G307" +"G308" +"G309" +"G310" +"G311" +"G312" +"G313" +"G314" +"G315" +"G316" +"G317" +"G318" +"G319" +"G320" +"G321" +"G322" +"G323" +"G324" +"G325" +"G326" +"G327" +"G328" +"G329" +"G330" +"G331" +"G332" +"G333" +"G334" diff --git a/Inferelator/jobs/bsubtilis_bbsr_high.R b/Inferelator/jobs/bsubtilis_bbsr_high.R new file mode 100644 index 0000000..347c608 --- /dev/null +++ b/Inferelator/jobs/bsubtilis_bbsr_high.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/bsubtilis' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 60 +PARS$delT.min <- 15 +PARS$tau <- 15 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'BBSR' +PARS$prior.weight <- 10 + +PARS$save.to.dir <- paste('output/bsubtilis', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/bsubtilis_bbsr_low.R b/Inferelator/jobs/bsubtilis_bbsr_low.R new file mode 100644 index 0000000..4e1e61d --- /dev/null +++ b/Inferelator/jobs/bsubtilis_bbsr_low.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/bsubtilis' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 60 +PARS$delT.min <- 15 +PARS$tau <- 15 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'BBSR' +PARS$prior.weight <- 1.6 + +PARS$save.to.dir <- paste('output/bsubtilis', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/bsubtilis_bbsr_noprior.R b/Inferelator/jobs/bsubtilis_bbsr_noprior.R new file mode 100644 index 0000000..da79d91 --- /dev/null +++ b/Inferelator/jobs/bsubtilis_bbsr_noprior.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/bsubtilis' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 60 +PARS$delT.min <- 15 +PARS$tau <- 15 + +PARS$perc.tp <- 0 +PARS$perm.tp <- 1 +PARS$perc.fp <- 0 +PARS$perm.fp <- 1 + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'BBSR' +PARS$prior.weight <- NULL + +PARS$save.to.dir <- paste('output/bsubtilis', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/bsubtilis_men_high.R b/Inferelator/jobs/bsubtilis_men_high.R new file mode 100644 index 0000000..b7f63cd --- /dev/null +++ b/Inferelator/jobs/bsubtilis_men_high.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/bsubtilis' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 60 +PARS$delT.min <- 15 +PARS$tau <- 15 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'MEN' +PARS$prior.weight <- 0.01 + +PARS$save.to.dir <- paste('output/bsubtilis', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/bsubtilis_men_low.R b/Inferelator/jobs/bsubtilis_men_low.R new file mode 100644 index 0000000..3cd6b59 --- /dev/null +++ b/Inferelator/jobs/bsubtilis_men_low.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/bsubtilis' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 60 +PARS$delT.min <- 15 +PARS$tau <- 15 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'MEN' +PARS$prior.weight <- 0.5 + +PARS$save.to.dir <- paste('output/bsubtilis', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/bsubtilis_men_noprior.R b/Inferelator/jobs/bsubtilis_men_noprior.R new file mode 100644 index 0000000..cce7cd9 --- /dev/null +++ b/Inferelator/jobs/bsubtilis_men_noprior.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/bsubtilis' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 60 +PARS$delT.min <- 15 +PARS$tau <- 15 + +PARS$perc.tp <- 0 +PARS$perm.tp <- 1 +PARS$perc.fp <- 0 +PARS$perm.fp <- 1 + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'MEN' +PARS$prior.weight <- NULL + +PARS$save.to.dir <- paste('output/bsubtilis', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/dream4_bbsr_high.R b/Inferelator/jobs/dream4_bbsr_high.R new file mode 100644 index 0000000..666015b --- /dev/null +++ b/Inferelator/jobs/dream4_bbsr_high.R @@ -0,0 +1,24 @@ +PARS$input.dir <- 'input/dream4' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 50 +PARS$cores <- 12 + +PARS$delT.max <- 110 +PARS$delT.min <- 0 +PARS$tau <- 45 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- FALSE + +PARS$method <- 'BBSR' +PARS$prior.weight <- 2.8 + +PARS$save.to.dir <- paste('output/dream4', PARS$method, PARS$prior.weight, sep='_') diff --git a/Inferelator/jobs/dream4_bbsr_low.R b/Inferelator/jobs/dream4_bbsr_low.R new file mode 100644 index 0000000..486d7fa --- /dev/null +++ b/Inferelator/jobs/dream4_bbsr_low.R @@ -0,0 +1,24 @@ +PARS$input.dir <- 'input/dream4' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 50 +PARS$cores <- 12 + +PARS$delT.max <- 110 +PARS$delT.min <- 0 +PARS$tau <- 45 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- FALSE + +PARS$method <- 'BBSR' +PARS$prior.weight <- 1.26 + +PARS$save.to.dir <- paste('output/dream4', PARS$method, PARS$prior.weight, sep='_') diff --git a/Inferelator/jobs/dream4_bbsr_noprior.R b/Inferelator/jobs/dream4_bbsr_noprior.R new file mode 100644 index 0000000..ccdff6f --- /dev/null +++ b/Inferelator/jobs/dream4_bbsr_noprior.R @@ -0,0 +1,24 @@ +PARS$input.dir <- 'input/dream4' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 50 +PARS$cores <- 12 + +PARS$delT.max <- 110 +PARS$delT.min <- 0 +PARS$tau <- 45 + +PARS$perc.tp <- 0 +PARS$perm.tp <- 1 +PARS$perc.fp <- 0 +PARS$perm.fp <- 1 + +PARS$eval.on.subset <- FALSE + +PARS$method <- 'BBSR' +PARS$prior.weight <- NULL + +PARS$save.to.dir <- paste('output/dream4_110_0_45', PARS$method, PARS$prior.weight, sep='_') diff --git a/Inferelator/jobs/dream4_men_high.R b/Inferelator/jobs/dream4_men_high.R new file mode 100644 index 0000000..3c0502d --- /dev/null +++ b/Inferelator/jobs/dream4_men_high.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/dream4' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 50 +PARS$cores <- 12 + +PARS$delT.max <- 110 +PARS$delT.min <- 0 +PARS$tau <- 45 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- FALSE + +PARS$method <- 'MEN' +PARS$prior.weight <- 0.01 + +PARS$save.to.dir <- paste('output/dream4', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/dream4_men_low.R b/Inferelator/jobs/dream4_men_low.R new file mode 100644 index 0000000..f75d048 --- /dev/null +++ b/Inferelator/jobs/dream4_men_low.R @@ -0,0 +1,24 @@ +PARS$input.dir <- 'input/dream4' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 50 +PARS$cores <- 12 + +PARS$delT.max <- 110 +PARS$delT.min <- 0 +PARS$tau <- 45 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- FALSE + +PARS$method <- 'MEN' +PARS$prior.weight <- 0.5 + +PARS$save.to.dir <- paste('output/dream4', PARS$method, PARS$prior.weight, sep='_') diff --git a/Inferelator/jobs/dream4_men_noprior.R b/Inferelator/jobs/dream4_men_noprior.R new file mode 100644 index 0000000..8b520ab --- /dev/null +++ b/Inferelator/jobs/dream4_men_noprior.R @@ -0,0 +1,24 @@ +PARS$input.dir <- 'input/dream4' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 50 +PARS$cores <- 12 + +PARS$delT.max <- 110 +PARS$delT.min <- 0 +PARS$tau <- 45 + +PARS$perc.tp <- 0 +PARS$perm.tp <- 1 +PARS$perc.fp <- 0 +PARS$perm.fp <- 1 + +PARS$eval.on.subset <- FALSE + +PARS$method <- 'MEN' +PARS$prior.weight <- NULL + +PARS$save.to.dir <- paste('output/dream4', PARS$method, PARS$prior.weight, sep='_') diff --git a/Inferelator/jobs/dream4_simplified.R b/Inferelator/jobs/dream4_simplified.R new file mode 100644 index 0000000..093de62 --- /dev/null +++ b/Inferelator/jobs/dream4_simplified.R @@ -0,0 +1,24 @@ +PARS$input.dir <- 'input/dream4' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 1 +PARS$cores <- 12 + +PARS$delT.max <- 110 +PARS$delT.min <- 0 +PARS$tau <- 45 + +PARS$perc.tp <- 50 # rep(50, 4) +PARS$perm.tp <- 1 # rep(1, 4) +PARS$perc.fp <- 0 # c(0, 100, 250, 500) +PARS$perm.fp <- 1 # c(1, 5, 5, 5) + +PARS$eval.on.subset <- FALSE + +PARS$method <- 'BBSR' +PARS$prior.weight <- 1.26 + +PARS$save.to.dir <- paste('output/dream4', PARS$method, PARS$prior.weight, sep='_') diff --git a/Inferelator/jobs/ecoli_bbsr_high.R b/Inferelator/jobs/ecoli_bbsr_high.R new file mode 100644 index 0000000..3ac179f --- /dev/null +++ b/Inferelator/jobs/ecoli_bbsr_high.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/ecoli' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 270 +PARS$delT.min <- 30 +PARS$tau <- 20 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'BBSR' +PARS$prior.weight <- 13 + +PARS$save.to.dir <- paste('output/ecoli', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/ecoli_bbsr_low.R b/Inferelator/jobs/ecoli_bbsr_low.R new file mode 100644 index 0000000..2ce58f0 --- /dev/null +++ b/Inferelator/jobs/ecoli_bbsr_low.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/ecoli' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 270 +PARS$delT.min <- 30 +PARS$tau <- 20 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'BBSR' +PARS$prior.weight <- 2.2 + +PARS$save.to.dir <- paste('output/ecoli', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/ecoli_bbsr_noprior.R b/Inferelator/jobs/ecoli_bbsr_noprior.R new file mode 100644 index 0000000..f0c9fc7 --- /dev/null +++ b/Inferelator/jobs/ecoli_bbsr_noprior.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/ecoli' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 270 +PARS$delT.min <- 30 +PARS$tau <- 20 + +PARS$perc.tp <- 0 +PARS$perm.tp <- 1 +PARS$perc.fp <- 0 +PARS$perm.fp <- 1 + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'BBSR' +PARS$prior.weight <- NULL + +PARS$save.to.dir <- paste('output/ecoli', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/ecoli_men_high.R b/Inferelator/jobs/ecoli_men_high.R new file mode 100644 index 0000000..5ebf792 --- /dev/null +++ b/Inferelator/jobs/ecoli_men_high.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/ecoli' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 270 +PARS$delT.min <- 30 +PARS$tau <- 20 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'MEN' +PARS$prior.weight <- 0.01 + +PARS$save.to.dir <- paste('output/ecoli', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/ecoli_men_low.R b/Inferelator/jobs/ecoli_men_low.R new file mode 100644 index 0000000..0c67c98 --- /dev/null +++ b/Inferelator/jobs/ecoli_men_low.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/ecoli' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 270 +PARS$delT.min <- 30 +PARS$tau <- 20 + +PARS$perc.tp <- rep(50, 4) +PARS$perm.tp <- rep(1, 4) +PARS$perc.fp <- c(0, 100, 250, 500) +PARS$perm.fp <- c(1, 5, 5, 5) + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'MEN' +PARS$prior.weight <- 0.5 + +PARS$save.to.dir <- paste('output/ecoli', PARS$method, PARS$prior.weight, sep='_') + diff --git a/Inferelator/jobs/ecoli_men_noprior.R b/Inferelator/jobs/ecoli_men_noprior.R new file mode 100644 index 0000000..c6816ef --- /dev/null +++ b/Inferelator/jobs/ecoli_men_noprior.R @@ -0,0 +1,25 @@ +PARS$input.dir <- 'input/ecoli' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 20 +PARS$cores <- 12 + +PARS$delT.max <- 270 +PARS$delT.min <- 30 +PARS$tau <- 20 + +PARS$perc.tp <- 0 +PARS$perm.tp <- 1 +PARS$perc.fp <- 0 +PARS$perm.fp <- 1 + +PARS$eval.on.subset <- TRUE + +PARS$method <- 'MEN' +PARS$prior.weight <- NULL + +PARS$save.to.dir <- paste('output/ecoli', PARS$method, PARS$prior.weight, sep='_') + From 8715af18f5a81cc0eb444cde069ccb3f15313c08 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Wed, 20 Jul 2016 14:31:27 -0400 Subject: [PATCH 02/25] disable inferelator.R reports --- Inferelator/inferelator.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index a63eb1b..63f56b5 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -65,9 +65,9 @@ PARS$prior.weight <- 1 PARS$use.tfa <- FALSE PARS$prior.ss <- FALSE -PARS$output.summary <- TRUE -PARS$output.report <- TRUE -PARS$output.tf.plots <- TRUE +PARS$output.summary <- FALSE +PARS$output.report <- FALSE +PARS$output.tf.plots <- FALSE # some of the elastic net parameters that are essentially constants; From 4a22648771888a80041e1192dc745d756d32ce8d Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Wed, 20 Jul 2016 14:43:33 -0400 Subject: [PATCH 03/25] Refactor call to subprocess for reuse. --- inferelator_ng/utils.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/inferelator_ng/utils.py b/inferelator_ng/utils.py index 5cb0f35..91a4fa7 100644 --- a/inferelator_ng/utils.py +++ b/inferelator_ng/utils.py @@ -28,11 +28,15 @@ def call_R(driver_path): Run an "R" script in a subprocess. Any outputs of the script should be saved to files. """ + return call_subprocess('R', '-f', driver_path) + +def call_subprocess(*command_line): if os.name == "posix": - command = "R -f " + driver_path + #command = "R -f " + driver_path + command = " ".join(command_line) return subprocess.check_output(command, shell=True) else: - theproc = subprocess.Popen(['R', '-f', driver_path]) + theproc = subprocess.Popen(command_line) return theproc.communicate() From f950fe5aa5466239d150e90580412473d163bb36 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Wed, 20 Jul 2016 15:16:35 -0400 Subject: [PATCH 04/25] simple driver for running inferelator.R with no validation yet --- inferelator_ng/inferelator_R.py | 17 +++++++++++++++++ inferelator_ng/tests/test_inferelator_R.py | 9 +++++++++ 2 files changed, 26 insertions(+) create mode 100644 inferelator_ng/inferelator_R.py create mode 100644 inferelator_ng/tests/test_inferelator_R.py diff --git a/inferelator_ng/inferelator_R.py b/inferelator_ng/inferelator_R.py new file mode 100644 index 0000000..44160c9 --- /dev/null +++ b/inferelator_ng/inferelator_R.py @@ -0,0 +1,17 @@ +""" +Utility for invoking R inferelator implementaion. +""" + +import os +import shutil +from . import utils + +INFERELATOR_R = utils.local_path("..", "Inferelator", "inferelator.R") + +def run_inferelator_R(job_name="dream4_simplified.R"): + root_path = utils.local_path("..", "Inferelator") + job_path = utils.local_path("..", "Inferelator", "jobs", job_name) + output_path = utils.local_path("..", "Inferelator", "output") + shutil.rmtree(output_path) + os.chdir(root_path) + return utils.call_subprocess("Rscript", INFERELATOR_R, job_path) diff --git a/inferelator_ng/tests/test_inferelator_R.py b/inferelator_ng/tests/test_inferelator_R.py new file mode 100644 index 0000000..578cf5e --- /dev/null +++ b/inferelator_ng/tests/test_inferelator_R.py @@ -0,0 +1,9 @@ + +import unittest, os + +from .. import inferelator_R + +class TestDreamSimple(unittest.TestCase): + + def test_run(self): + inferelator_R.run_inferelator_R("dream4_simplified.R") From d6203d2d06fdcc71e5be5717cfd9b6784d4d8e60 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Wed, 20 Jul 2016 15:25:10 -0400 Subject: [PATCH 05/25] test folder exists before rmtree --- inferelator_ng/inferelator_R.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/inferelator_ng/inferelator_R.py b/inferelator_ng/inferelator_R.py index 44160c9..40410ac 100644 --- a/inferelator_ng/inferelator_R.py +++ b/inferelator_ng/inferelator_R.py @@ -1,5 +1,6 @@ """ -Utility for invoking R inferelator implementaion. +Utility for invoking R inferelator implementation. +Only one run at a time is permitted. """ import os @@ -12,6 +13,7 @@ def run_inferelator_R(job_name="dream4_simplified.R"): root_path = utils.local_path("..", "Inferelator") job_path = utils.local_path("..", "Inferelator", "jobs", job_name) output_path = utils.local_path("..", "Inferelator", "output") - shutil.rmtree(output_path) + if os.path.exists(output_path): + shutil.rmtree(output_path) os.chdir(root_path) return utils.call_subprocess("Rscript", INFERELATOR_R, job_path) From 12ef12a5944f26fb935985ce80865ca6e126202e Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Wed, 20 Jul 2016 15:40:31 -0400 Subject: [PATCH 06/25] add failure hook for travis debugging --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index c0fc3bc..36e6ad4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -33,6 +33,7 @@ after_success: - codecov after_failure: - pwd + - which Rscript - find . - cat ./inferelator_ng/tests/artifacts/run_mi.R - R -f ./inferelator_ng/tests/artifacts/run_mi.R From d7d8a65aabd25b88a823b19a76755001403e2d52 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Wed, 20 Jul 2016 15:56:31 -0400 Subject: [PATCH 07/25] missing ggplot2 package --- inferelator_ng/R_code/packages.R | 1 + 1 file changed, 1 insertion(+) diff --git a/inferelator_ng/R_code/packages.R b/inferelator_ng/R_code/packages.R index f215f82..7bce280 100644 --- a/inferelator_ng/R_code/packages.R +++ b/inferelator_ng/R_code/packages.R @@ -2,3 +2,4 @@ install.packages('inline', repos='http://cran.us.r-project.org') install.packages('parallel', repos='http://cran.us.r-project.org') +install.packages('ggplot2', repos='http://cran.us.r-project.org') From 9adbed2d6243260a9a366ab069de68fd4cf0c828 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Wed, 20 Jul 2016 15:57:27 -0400 Subject: [PATCH 08/25] more checking and cleanup --- inferelator_ng/inferelator_R.py | 2 ++ inferelator_ng/utils.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/inferelator_ng/inferelator_R.py b/inferelator_ng/inferelator_R.py index 40410ac..8db1bbf 100644 --- a/inferelator_ng/inferelator_R.py +++ b/inferelator_ng/inferelator_R.py @@ -16,4 +16,6 @@ def run_inferelator_R(job_name="dream4_simplified.R"): if os.path.exists(output_path): shutil.rmtree(output_path) os.chdir(root_path) + assert os.path.exists(INFERELATOR_R) + assert os.path.exists(job_path) return utils.call_subprocess("Rscript", INFERELATOR_R, job_path) diff --git a/inferelator_ng/utils.py b/inferelator_ng/utils.py index 91a4fa7..f67d1eb 100644 --- a/inferelator_ng/utils.py +++ b/inferelator_ng/utils.py @@ -61,7 +61,7 @@ def path(self, filename): def local_path(*location): "Return location relative to the folder containing this module." - return r_path(os.path.join(my_dir, *location)) + return r_path(os.path.abspath(os.path.join(my_dir, *location))) def df_from_tsv(file_like): From 47e63d2635297a89dcd95d0af8360a879f8ac564 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Thu, 21 Jul 2016 09:38:53 -0400 Subject: [PATCH 09/25] remove ggplot2 dependancy for travis runs --- Inferelator/inferelator.R | 22 +++++++++++----------- inferelator_ng/R_code/packages.R | 3 ++- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index 63f56b5..5ff8f06 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -20,7 +20,7 @@ source('R_scripts/evaluate.R') source('R_scripts/tfa.R') source('R_scripts/group_predictors.R') source('R_scripts/summarize_results.R') -source('R_scripts/vis_tfs_and_targets.R') +#source('R_scripts/vis_tfs_and_targets.R') date.time.str <- format(Sys.time(), "%Y-%m-%d_%H-%M-%S") @@ -429,16 +429,16 @@ PROCTIME <- proc.time() - start.proc.time save(PARS, IN, SEED, PROCTIME, file = paste(PARS$save.to.dir, "/params_and_input.RData", sep="")) # generate network report and visualize TFs and targets -source('R_scripts/net_report_new.R') -Sys.sleep(2) -for (ccf in list.files(PARS$save.to.dir, pattern='combinedconf_*', full.names=TRUE)) { - if (PARS$output.report) { - net.report(normalizePath(ccf)) - } - if (PARS$output.tf.plots) { - vis.tfs.and.targets(normalizePath(ccf), CORES=PARS$cores/2) - } -} +#source('R_scripts/net_report_new.R') +#Sys.sleep(2) +#for (ccf in list.files(PARS$save.to.dir, pattern='combinedconf_*', full.names=TRUE)) { +# if (PARS$output.report) { +# net.report(normalizePath(ccf)) +# } +# if (PARS$output.tf.plots) { +# vis.tfs.and.targets(normalizePath(ccf), CORES=PARS$cores/2) +# } +#} # this part does not work for grouped predictors #if (!is.null(IN$gs.mat)) { diff --git a/inferelator_ng/R_code/packages.R b/inferelator_ng/R_code/packages.R index 7bce280..d5f6209 100644 --- a/inferelator_ng/R_code/packages.R +++ b/inferelator_ng/R_code/packages.R @@ -2,4 +2,5 @@ install.packages('inline', repos='http://cran.us.r-project.org') install.packages('parallel', repos='http://cran.us.r-project.org') -install.packages('ggplot2', repos='http://cran.us.r-project.org') +# This doesn't work on travis: +# install.packages('ggplot2', repos='http://cran.us.r-project.org') From 23a36f9a236fa868a91ad1bf2cba913d3dd69566 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Thu, 21 Jul 2016 11:40:41 -0400 Subject: [PATCH 10/25] check that output files get created in inferelator test --- inferelator_ng/inferelator_R.py | 3 ++- inferelator_ng/tests/test_inferelator_R.py | 21 ++++++++++++++++++--- 2 files changed, 20 insertions(+), 4 deletions(-) diff --git a/inferelator_ng/inferelator_R.py b/inferelator_ng/inferelator_R.py index 8db1bbf..4b4e158 100644 --- a/inferelator_ng/inferelator_R.py +++ b/inferelator_ng/inferelator_R.py @@ -18,4 +18,5 @@ def run_inferelator_R(job_name="dream4_simplified.R"): os.chdir(root_path) assert os.path.exists(INFERELATOR_R) assert os.path.exists(job_path) - return utils.call_subprocess("Rscript", INFERELATOR_R, job_path) + utils.call_subprocess("Rscript", INFERELATOR_R, job_path) + return output_path diff --git a/inferelator_ng/tests/test_inferelator_R.py b/inferelator_ng/tests/test_inferelator_R.py index 578cf5e..ec9288c 100644 --- a/inferelator_ng/tests/test_inferelator_R.py +++ b/inferelator_ng/tests/test_inferelator_R.py @@ -1,9 +1,24 @@ -import unittest, os +import unittest, os, glob from .. import inferelator_R class TestDreamSimple(unittest.TestCase): - def test_run(self): - inferelator_R.run_inferelator_R("dream4_simplified.R") + def test_simplified_run(self): + output_path = inferelator_R.run_inferelator_R("dream4_simplified.R") + # check that the output directory was created + assert os.path.exists(output_path) + # check that subdirectory exists + subdirs = glob.glob(os.path.join(output_path, "*")) + self.assertEqual(len(subdirs), 1, "one subdir only "+repr((output_path, subdirs))) + subdir = subdirs[0] + # check for Rdata output files + for (pattern, count) in [ + ("betas*.RData", 1), + ("combined*.RData", 1), + ("params*.RData", 1)]: + glob_path = os.path.join(subdir, pattern) + matches = glob.glob(glob_path) + self.assertEqual(len(matches), count, + "did not find matches " + repr((glob_path, count, matches))) From 6e2d6343dfd7c9e8c9ac536f66da753b3fbf19ca Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 10:06:59 -0400 Subject: [PATCH 11/25] remove bogus files --- Inferelator/input/.DS_Store | Bin 6148 -> 0 bytes Inferelator/input/bsubtilis/.DS_Store | Bin 6148 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 Inferelator/input/.DS_Store delete mode 100644 Inferelator/input/bsubtilis/.DS_Store diff --git a/Inferelator/input/.DS_Store b/Inferelator/input/.DS_Store deleted file mode 100644 index 307452c8514b0024166ae8db90c7a346882a6ae4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKI|>3p3{BKRv9YwAD|mxJ^aNf&RuKfjVzuAOb9pphK83Q{X(KO?yqQeiEc=Sh zMnrUZ-7iFDA~J>>%FROG?A*L(gUl!pjyo=LloPx??0ePk6ULoLBduH!pZM*rIiF8< zOrru+fC^9nDnJFks6aN?@$`##@+vAo1^z<;`#u!7VNL7;{nLTqBLHxOv>VnwO8|={ zfHkoTL$0iZB|RXQufx~%UQ22@GabG?r<}#or2))80hU78*9gNFN(Ti YYn<1_F3{=7I~~ZM0n>#>1%9o-1Fn=6qyPW_ diff --git a/Inferelator/input/bsubtilis/.DS_Store b/Inferelator/input/bsubtilis/.DS_Store deleted file mode 100644 index 14735de8559c7a734a2be05389e278d95da43bbc..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKyK2Kg5ZrZC7`t(q@_rx?rLfz>C|{5tFpfzTP!ZCr@^|IaGW#GPxh8xP&!qn{Cd}6E2C=iZ2p5$5fh^-tZ)wdJI-N*}gtuz1l zAD6H9bKiej0gVb!0V+TRr~nn1O@VB!>+Nhe@**lg1(u?KeIE+kuqFyQ5dhdA z?S{3_62M{!U`-qXk%4JYfkD+AF*NANm#nLaLtxNFbNJAFvgU-M{&d`5yj-*fGExC5 zFjn9pwma+puke57|1pU>DnJDmrGPfwTX)1OWpAClob}oQKf$f$2X2P7QxLoz1HBz% gW9|6Si=wXB8s{}}2y{B~P6zU5z;vNefqN_P3&|7_{r~^~ From 514fe77d0c28ef9a827b2b83795bf315d63c1818 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 10:40:57 -0400 Subject: [PATCH 12/25] add tsv dump for design and response --- Inferelator/inferelator.R | 6 ++++++ inferelator_ng/tests/test_inferelator_R.py | 1 + 2 files changed, 7 insertions(+) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index 5ff8f06..8d0c14b 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -171,6 +171,12 @@ des.res <- design.and.response(IN$meta.data, IN$exp.mat, PARS$delT.min, IN$final_response_matrix <- des.res$final_response_matrix IN$final_design_matrix <- des.res$final_design_matrix resp.idx <- des.res$resp.idx + +write.table(as.matrix(IN$final_response_matrix), + paste(PARS$save.to.dir, "/final_response_matrix.tsv", sep=""), sep = '\t') +write.table(as.matrix(IN$final_design_matrix), + paste(PARS$save.to.dir, "/final_design_matrix.tsv", sep=""), sep = '\t') + cat("done.\n") if (!all(apply(resp.idx, 1, identical, resp.idx[1,]))) { diff --git a/inferelator_ng/tests/test_inferelator_R.py b/inferelator_ng/tests/test_inferelator_R.py index ec9288c..7328819 100644 --- a/inferelator_ng/tests/test_inferelator_R.py +++ b/inferelator_ng/tests/test_inferelator_R.py @@ -6,6 +6,7 @@ class TestDreamSimple(unittest.TestCase): def test_simplified_run(self): + "execute: Rscript inferelator.R jobs/dream4_simplified.R" output_path = inferelator_R.run_inferelator_R("dream4_simplified.R") # check that the output directory was created assert os.path.exists(output_path) From 4c64294e7099180801d63c30efd7aeeb4097c974 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 11:18:07 -0400 Subject: [PATCH 13/25] add dump of priors --- Inferelator/inferelator.R | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index 8d0c14b..40177a2 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -195,6 +195,12 @@ IN$priors <- getPriors(IN$exp.mat, IN$tf.names, IN$priors.mat, IN$gs.mat, PARS$eval.on.subset, PARS$job.seed, PARS$perc.tp, PARS$perm.tp, PARS$perc.fp, PARS$perm.fp, PARS$pr.sel.mode) + +for (ii in 1:length(IN$priors)) { + thisprior <- IN$priors[[ii]] + write.table(as.matrix(thisprior), + paste(PARS$save.to.dir, "/prior", ii, ".tsv", sep=""), sep = '\t') +} cat("done.\n") From f4b54004cc1c0c48f41fcd525d586b5870e3c7ba Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 11:27:47 -0400 Subject: [PATCH 14/25] dump bootstrap matrix --- Inferelator/inferelator.R | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index 40177a2..c627e85 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -218,6 +218,10 @@ if (PARS$num.boots == 1) { IN$bs.pi[bootstrap, ] <- resp.idx[1, sample(ncol(resp.idx), replace=TRUE)] } } + +write.table(as.matrix(IN$bs.pi), + paste(PARS$save.to.dir, "/bootstrap_pi.tsv", sep=""), sep = '\t') + #IN$bs.prior.rm <- list() #if (PARS$num.boots == 1) { # IN$bs.prior.rm[[1]] <- c() From 10361ae7e969e9d0a14937450ddf6a638c149258 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 11:42:28 -0400 Subject: [PATCH 15/25] add simplified job that uses tfa --- Inferelator/inferelator.R | 6 ++++- Inferelator/jobs/dream4_usetfa_simplified.R | 26 +++++++++++++++++++++ inferelator_ng/R_code/packages.R | 1 + 3 files changed, 32 insertions(+), 1 deletion(-) create mode 100644 Inferelator/jobs/dream4_usetfa_simplified.R diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index c627e85..6686d8d 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -221,7 +221,7 @@ if (PARS$num.boots == 1) { write.table(as.matrix(IN$bs.pi), paste(PARS$save.to.dir, "/bootstrap_pi.tsv", sep=""), sep = '\t') - + #IN$bs.prior.rm <- list() #if (PARS$num.boots == 1) { # IN$bs.prior.rm[[1]] <- c() @@ -245,6 +245,10 @@ if(PARS$use.tfa) { PARS$delT.max, PARS$tau/2) IN$final_response_matrix_halftau <- des.res$final_response_matrix + + write.table(as.matrix(des.res$final_response_matrix), + paste(PARS$save.to.dir, "/final_response_matrix_halftau.tsv", sep=""), sep = '\t') + cat("done.\n") } diff --git a/Inferelator/jobs/dream4_usetfa_simplified.R b/Inferelator/jobs/dream4_usetfa_simplified.R new file mode 100644 index 0000000..b814180 --- /dev/null +++ b/Inferelator/jobs/dream4_usetfa_simplified.R @@ -0,0 +1,26 @@ +PARS$input.dir <- 'input/dream4' + +PARS$meta.data.file <- 'meta_data.tsv' +PARS$priors.file <- 'gold_standard.tsv' +PARS$gold.standard.file <- 'gold_standard.tsv' + +PARS$num.boots <- 1 +PARS$cores <- 12 + +PARS$delT.max <- 110 +PARS$delT.min <- 0 +PARS$tau <- 45 + +PARS$perc.tp <- 50 # rep(50, 4) +PARS$perm.tp <- 1 # rep(1, 4) +PARS$perc.fp <- 0 # c(0, 100, 250, 500) +PARS$perm.fp <- 1 # c(1, 5, 5, 5) + +PARS$eval.on.subset <- FALSE + +PARS$method <- 'BBSR' +PARS$prior.weight <- 1.26 + +PARS$save.to.dir <- paste('output/dream4', PARS$method, PARS$prior.weight, sep='_') + +PARS$use.tfa <- TRUE diff --git a/inferelator_ng/R_code/packages.R b/inferelator_ng/R_code/packages.R index d5f6209..8223a98 100644 --- a/inferelator_ng/R_code/packages.R +++ b/inferelator_ng/R_code/packages.R @@ -2,5 +2,6 @@ install.packages('inline', repos='http://cran.us.r-project.org') install.packages('parallel', repos='http://cran.us.r-project.org') +install.packages('corpcor', repos='http://cran.us.r-project.org') # This doesn't work on travis: # install.packages('ggplot2', repos='http://cran.us.r-project.org') From 2785e957e883b602d9809c1192f99197247540a5 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 11:47:50 -0400 Subject: [PATCH 16/25] add end-to-end run with tfa --- inferelator_ng/tests/test_inferelator_R.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/inferelator_ng/tests/test_inferelator_R.py b/inferelator_ng/tests/test_inferelator_R.py index 7328819..ba5ccb8 100644 --- a/inferelator_ng/tests/test_inferelator_R.py +++ b/inferelator_ng/tests/test_inferelator_R.py @@ -5,9 +5,9 @@ class TestDreamSimple(unittest.TestCase): - def test_simplified_run(self): + def test_simplified_run(self, parameters="dream4_simplified.R"): "execute: Rscript inferelator.R jobs/dream4_simplified.R" - output_path = inferelator_R.run_inferelator_R("dream4_simplified.R") + output_path = inferelator_R.run_inferelator_R(parameters) # check that the output directory was created assert os.path.exists(output_path) # check that subdirectory exists @@ -23,3 +23,6 @@ def test_simplified_run(self): matches = glob.glob(glob_path) self.assertEqual(len(matches), count, "did not find matches " + repr((glob_path, count, matches))) + + def test_simplified_with_TFA(self): + return self.test_simplified_run("dream4_usetfa_simplified.R") From bee94f837bdad446cd3ad7d0788281314ef7a32f Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 12:09:43 -0400 Subject: [PATCH 17/25] dump activities matrix --- Inferelator/inferelator.R | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index 6686d8d..72ae250 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -266,6 +266,10 @@ for (prior.name in names(IN$priors)) { # estimate transcription factor activities if(PARS$use.tfa) { prior.tf.activities <- tfa(prior, IN$final_design_matrix, IN$final_response_matrix_halftau) + + write.table(as.matrix(prior.tf.activities), + paste(PARS$save.to.dir, "/activities_", prior.name, ".tsv", sep=""), sep = '\t') + IN$tf.activities[[prior.name]] <- prior.tf.activities } From 74828cfdf8541fc9771e99ecc3c8a314e353c103 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 13:52:20 -0400 Subject: [PATCH 18/25] try to fix travis problem --- inferelator_ng/R_code/packages.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/inferelator_ng/R_code/packages.R b/inferelator_ng/R_code/packages.R index 8223a98..a2fae1d 100644 --- a/inferelator_ng/R_code/packages.R +++ b/inferelator_ng/R_code/packages.R @@ -2,6 +2,7 @@ install.packages('inline', repos='http://cran.us.r-project.org') install.packages('parallel', repos='http://cran.us.r-project.org') -install.packages('corpcor', repos='http://cran.us.r-project.org') +#install.packages('corpcor', repos='http://cran.us.r-project.org') -- doesn't work on travis +install.packages("corpcor", dependencies=TRUE, repos='http://cran.rstudio.com/') # This doesn't work on travis: # install.packages('ggplot2', repos='http://cran.us.r-project.org') From 96967db21228f5ec18502d2cfadae86f19a7e00d Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 14:07:47 -0400 Subject: [PATCH 19/25] skip test that requires corpcore R package in TRAVIS for now. --- .travis.yml | 1 + inferelator_ng/tests/test_inferelator_R.py | 2 ++ 2 files changed, 3 insertions(+) diff --git a/.travis.yml b/.travis.yml index 36e6ad4..95adfe5 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,6 +26,7 @@ install: # install R components - sudo R -f inferelator_ng/R_code/packages.R script: + - export TRAVIS_TESTING=noninteractive - python -c "import os; print(repr(os.name))" - coverage run --source=inferelator_ng setup.py test #- nosetests diff --git a/inferelator_ng/tests/test_inferelator_R.py b/inferelator_ng/tests/test_inferelator_R.py index ba5ccb8..8109694 100644 --- a/inferelator_ng/tests/test_inferelator_R.py +++ b/inferelator_ng/tests/test_inferelator_R.py @@ -24,5 +24,7 @@ def test_simplified_run(self, parameters="dream4_simplified.R"): self.assertEqual(len(matches), count, "did not find matches " + repr((glob_path, count, matches))) + @unittest.skipIf(os.environ.has_key("TRAVIS_TESTING"), + "Skipping because of failure to install 'corpcor' R package in Travis") def test_simplified_with_TFA(self): return self.test_simplified_run("dream4_usetfa_simplified.R") From a1ac0f8edd75f2cc2e2b78811d3bf6e371d337ab Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 14:15:40 -0400 Subject: [PATCH 20/25] py3 fix --- inferelator_ng/tests/test_inferelator_R.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/inferelator_ng/tests/test_inferelator_R.py b/inferelator_ng/tests/test_inferelator_R.py index 8109694..796b172 100644 --- a/inferelator_ng/tests/test_inferelator_R.py +++ b/inferelator_ng/tests/test_inferelator_R.py @@ -24,7 +24,7 @@ def test_simplified_run(self, parameters="dream4_simplified.R"): self.assertEqual(len(matches), count, "did not find matches " + repr((glob_path, count, matches))) - @unittest.skipIf(os.environ.has_key("TRAVIS_TESTING"), + @unittest.skipIf("TRAVIS_TESTING" in os.environ, "Skipping because of failure to install 'corpcor' R package in Travis") def test_simplified_with_TFA(self): return self.test_simplified_run("dream4_usetfa_simplified.R") From 2036fad16f6a19555a5c46d0cd06804585d50ffb Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 15:00:08 -0400 Subject: [PATCH 21/25] comment dead code and dump X Y --- Inferelator/inferelator.R | 66 +++++++++++++++++++++------------------ 1 file changed, 35 insertions(+), 31 deletions(-) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index 72ae250..ad18385 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -298,36 +298,37 @@ for (prior.name in names(IN$priors)) { # set to FALSE to disable the next block not.done <- PARS$prior.ss gp.out.bs <- gp.out - while (not.done) { - not.done <- FALSE - - # set some of the prior interactions to zero - prior <- gp.out$prior.mat - to.zero <- setdiff(1:length(prior), sample(length(prior), replace=TRUE)) - cat(sprintf('Setting %d non-zero prior edges to zero\n', sum(prior[to.zero] != 0))) - prior[to.zero] <- 0 - - # estimate transcription factor activities - if(PARS$use.tfa) { - des.mat <- rbind(IN$final_design_matrix, group.expression(gp.out$pred.groups, IN$final_design_matrix)) - IN$tf.activities.bs[[prior.name]][[bootstrap]] <- tfa(prior, des.mat, IN$final_response_matrix_halftau) - } - - des.mat <- IN$final_design_matrix[IN$tf.with.expr, ] - if(PARS$use.tfa) { - des.mat <- IN$tf.activities.bs[[prior.name]][[bootstrap]][gp.out$tf.names, ] - } - - # group again? what if we group groups? - #cat("group predictors\n") - #gp.out.bs <- group.predictors(des.mat, prior, IN$gs.mat, IN$bs.pi, cor.th=0.99, grp.pre='pred.group.bs.') - gp.out.bs <- group.predictors(des.mat, prior, gp.out$gs.mat, IN$bs.pi, cor.th=0.99, grp.pre='pred.group.bs.') - #IN$grouped.pred.bs[[prior.name]][[bootstrap]] <- gp.out.bs - if (length(gp.out.bs$pred.groups) > 0) { - cat('grouped predictors - subsample from prior again\n') - not.done <- TRUE - } - } + + # Commenting the next block (never executed in known cases) + #while (not.done) { + # not.done <- FALSE + # + # # set some of the prior interactions to zero + # prior <- gp.out$prior.mat + # to.zero <- setdiff(1:length(prior), sample(length(prior), replace=TRUE)) + # cat(sprintf('Setting %d non-zero prior edges to zero\n', sum(prior[to.zero] != 0))) + # prior[to.zero] <- 0 + # + # # estimate transcription factor activities + # if(PARS$use.tfa) { + # des.mat <- rbind(IN$final_design_matrix, group.expression(gp.out$pred.groups, IN$final_design_matrix)) + # IN$tf.activities.bs[[prior.name]][[bootstrap]] <- tfa(prior, des.mat, IN$final_response_matrix_halftau) + # } + # + # des.mat <- IN$final_design_matrix[IN$tf.with.expr, ] + # if(PARS$use.tfa) { + # des.mat <- IN$tf.activities.bs[[prior.name]][[bootstrap]][gp.out$tf.names, ] + # } + # # group again? what if we group groups? + # #cat("group predictors\n") + # #gp.out.bs <- group.predictors(des.mat, prior, IN$gs.mat, IN$bs.pi, cor.th=0.99, grp.pre='pred.group.bs.') + # gp.out.bs <- group.predictors(des.mat, prior, gp.out$gs.mat, IN$bs.pi, cor.th=0.99, grp.pre='pred.group.bs.') + # #IN$grouped.pred.bs[[prior.name]][[bootstrap]] <- gp.out.bs + # if (length(gp.out.bs$pred.groups) > 0) { + # cat('grouped predictors - subsample from prior again\n') + # not.done <- TRUE + # } + #} # set the prior weights matrix @@ -352,7 +353,10 @@ for (prior.name in names(IN$priors)) { #X <- IN$final_design_matrix[, IN$bs.pi[bootstrap, ]] X <- gp.out.bs$des.mat[, IN$bs.pi[bootstrap, ]] Y <- IN$final_response_matrix[, IN$bs.pi[bootstrap, ]] - + write.table(as.matrix(X), + paste(PARS$save.to.dir, "/X_", prior.name, "_", bootstrap, ".tsv", sep=""), sep = '\t') + write.table(as.matrix(Y), + paste(PARS$save.to.dir, "/Y_", prior.name, "_", bootstrap, ".tsv", sep=""), sep = '\t') if (nrow(X) > 6000) { #X <- X[IN$tf.names, ] # speeds up MI calculation for large datasets X <- X[gp.out.bs$tf.names, ] From dbc2ba0f2cf2d897e30182d458924f3f0e8a66de Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 15:13:19 -0400 Subject: [PATCH 22/25] dump mutual information ms_bg --- Inferelator/inferelator.R | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index ad18385..1939a14 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -353,10 +353,12 @@ for (prior.name in names(IN$priors)) { #X <- IN$final_design_matrix[, IN$bs.pi[bootstrap, ]] X <- gp.out.bs$des.mat[, IN$bs.pi[bootstrap, ]] Y <- IN$final_response_matrix[, IN$bs.pi[bootstrap, ]] + write.table(as.matrix(X), paste(PARS$save.to.dir, "/X_", prior.name, "_", bootstrap, ".tsv", sep=""), sep = '\t') write.table(as.matrix(Y), paste(PARS$save.to.dir, "/Y_", prior.name, "_", bootstrap, ".tsv", sep=""), sep = '\t') + if (nrow(X) > 6000) { #X <- X[IN$tf.names, ] # speeds up MI calculation for large datasets X <- X[gp.out.bs$tf.names, ] @@ -373,6 +375,8 @@ for (prior.name in names(IN$priors)) { cat("Calculating Background MI\n") Ms_bg <- mi(t(X), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores) diag(Ms_bg) <- 0 + write.table(as.matrix(X), + paste(PARS$save.to.dir, "/Ms_bg_", prior.name, "_", bootstrap, ".tsv", sep=""), sep = '\t') # get CLR matrix cat("Calculating CLR Matrix\n") From 0e7d1a49258d1477da60cf5fba45018922de8bd4 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Tue, 26 Jul 2016 15:18:01 -0400 Subject: [PATCH 23/25] dump clr matrix --- Inferelator/inferelator.R | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index 1939a14..9624f75 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -375,7 +375,7 @@ for (prior.name in names(IN$priors)) { cat("Calculating Background MI\n") Ms_bg <- mi(t(X), t(X), nbins=PARS$mi.bins, cpu.n=PARS$cores) diag(Ms_bg) <- 0 - write.table(as.matrix(X), + write.table(as.matrix(Ms_bg), paste(PARS$save.to.dir, "/Ms_bg_", prior.name, "_", bootstrap, ".tsv", sep=""), sep = '\t') # get CLR matrix @@ -385,6 +385,9 @@ for (prior.name in names(IN$priors)) { #clr.mat <- clr.mat[, IN$tf.names] clr.mat <- clr.mat[, gp.out.bs$tf.names] + write.table(as.matrix(clr.mat), + paste(PARS$save.to.dir, "/CLR_", prior.name, "_", bootstrap, ".tsv", sep=""), sep = '\t') + # DREAM8 induced change: #for (tf1 in IN$tf.names) { # for (tf2 in IN$tf.names) { From e7d77e7071d579e2fa78d51da806ad4be75d4115 Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Thu, 28 Jul 2016 14:13:00 -0400 Subject: [PATCH 24/25] save correct matrix --- Inferelator/inferelator.R | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Inferelator/inferelator.R b/Inferelator/inferelator.R index 9624f75..3a28d2d 100644 --- a/Inferelator/inferelator.R +++ b/Inferelator/inferelator.R @@ -246,7 +246,7 @@ if(PARS$use.tfa) { IN$final_response_matrix_halftau <- des.res$final_response_matrix - write.table(as.matrix(des.res$final_response_matrix), + write.table(as.matrix(des.res$final_response_matrix_halftau), paste(PARS$save.to.dir, "/final_response_matrix_halftau.tsv", sep=""), sep = '\t') cat("done.\n") @@ -454,6 +454,9 @@ for (prior.name in names(IN$priors)) { comb.confs <- Matrix((comb.confs - min(comb.confs)) / (PARS$num.boots * length(comb.confs) - min(comb.confs))) save(comb.confs, file=confs.file) + write.table(as.matrix(comb.confs), + paste(PARS$save.to.dir, "/cconf_", prior.name, ".tsv", sep=""), sep = '\t') + if (PARS$output.summary) { sum.net(betas, betas.resc, comb.confs, IN, confs.file) } From 090ed5e783a4dc5f36a9c23df82945bf56284a9a Mon Sep 17 00:00:00 2001 From: Aaron Watters Date: Thu, 28 Jul 2016 14:14:04 -0400 Subject: [PATCH 25/25] comment out non-working package --- inferelator_ng/R_code/packages.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/inferelator_ng/R_code/packages.R b/inferelator_ng/R_code/packages.R index a2fae1d..a5c6422 100644 --- a/inferelator_ng/R_code/packages.R +++ b/inferelator_ng/R_code/packages.R @@ -3,6 +3,6 @@ install.packages('inline', repos='http://cran.us.r-project.org') install.packages('parallel', repos='http://cran.us.r-project.org') #install.packages('corpcor', repos='http://cran.us.r-project.org') -- doesn't work on travis -install.packages("corpcor", dependencies=TRUE, repos='http://cran.rstudio.com/') +#install.packages("corpcor", dependencies=TRUE, repos='http://cran.rstudio.com/') -- neither does this # This doesn't work on travis: # install.packages('ggplot2', repos='http://cran.us.r-project.org')