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# Conflicts: # src/main/java/org/matsim/episim/analysis/AnalyzeSnzData.java # src/main/java/org/matsim/run/batch/BerlinPercolation.java # src/main/java/org/matsim/run/batch/StrainPaperAdaptivePolicy.java # src/main/java/org/matsim/run/modules/SnzBerlinProductionScenario.java # src/main/java/org/matsim/run/modules/SnzDresdenScenario.java # src/main/java/org/matsim/run/modules/SyntheticScenario.java # src/main/java/org/matsim/scenarioCreation/RunTrial.java # src/main/python/analysis/percolation.py
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196 changes: 99 additions & 97 deletions
196
src/main/python/analysis/dispersion.R → src/main/R/dispersion.R
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,97 +1,99 @@ | ||
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| library(fitdistrplus) | ||
| library(dplyr) | ||
| library(purrr) | ||
| library(ggplot2) | ||
| library(reticulate) | ||
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| use_miniconda(required = TRUE) | ||
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| np <- import("numpy") | ||
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| f <- "C:/home/Development/matsim-org/matsim-episim/output-berlin-25pct-superSpreader-calibrParam-1.65E-5/infectionEvents.txt" | ||
| f <- "C:/home/Development/matsim-org/matsim-episim/output-berlin-25pct-superSpreader-calibrParam-2.7E-5/infectionEvents.txt" | ||
| f <- "C:/home/Development/matsim-org/matsim-episim/output-base/infectionEvents.txt" | ||
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| counts <- function(f) { | ||
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| data <- read.csv(f, sep = "\t") | ||
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| df <- table(data[[2]]) | ||
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| v <- as.numeric(df) | ||
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| # Persons who did not infect other persons | ||
| no_inf <- setdiff(data$infected, data$infector) | ||
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| res <- sort(c(rep(0, length(no_inf)), v)) | ||
| } | ||
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| est_disp <- function(f) { | ||
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| matrix <- np$load(f) | ||
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| est <- list() | ||
| inf80 <- list() | ||
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| for(row in 1:nrow(matrix)) { | ||
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| sprintf("Processing row %d\n", row) | ||
| v <- matrix[row,] | ||
| v <- v[!is.na(v)] | ||
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| if (length(v) == 0) { | ||
| next | ||
| } | ||
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| fit <- try(fitdist(v, fix.arg=list(mu=2.5), "nbinom"), silent = T) | ||
| if(inherits(fit, "try-error")) { | ||
| fit <- list(estimate=NaN) | ||
| } | ||
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| est <- c(est, as.numeric(fit$estimate)) | ||
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| total = sum(v) | ||
| s80 <- v[0:-length(v) * 0.8] | ||
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| inf80 <- c(inf80, sum(s80) * 100 / total) | ||
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| } | ||
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| df <- data.frame(as.numeric(est), as.numeric(inf80)) | ||
| colnames(df) <- c("est", "top20") | ||
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| return(df) | ||
| } | ||
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| est_disp_zip <- function(f) { | ||
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| tmpdir <- tempdir() | ||
| unzip(f, exdir = tmpdir ) | ||
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| res <- data.frame() | ||
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| for (f in list.files(tmpdir, pattern = "*.npy", full.names = T)) { | ||
| df <- est_disp(f) | ||
| means <- colMeans(df, na.rm = T) | ||
| df <- data.frame(t(means)) | ||
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| row.names(df) <- c(basename(f)) | ||
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| res <- rbind(res, df) | ||
| } | ||
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| return(res) | ||
| } | ||
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| setwd("C:/home/Development/matsim-org/matsim-episim/src/main/python/analysis") | ||
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| df <- est_disp_zip("data/infections.zip") | ||
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| # Testing the distribution | ||
| df <- sort(rnbinom(15000, size=1, mu=2.5)) | ||
| plot(hist(df, breaks = 30)) | ||
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| ggplot() + aes(df) + geom_histogram(binwidth=1, colour="black", fill="white", alpha=0.2) | ||
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| library(fitdistrplus) | ||
| library(dplyr) | ||
| library(purrr) | ||
| library(ggplot2) | ||
| library(reticulate) | ||
|
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| use_miniconda(required = TRUE) | ||
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| np <- import("numpy") | ||
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| f <- "C:/home/Development/matsim-org/matsim-episim/output-berlin-25pct-superSpreader-calibrParam-1.65E-5/infectionEvents.txt" | ||
| f <- "C:/home/Development/matsim-org/matsim-episim/output-berlin-25pct-superSpreader-calibrParam-2.7E-5/infectionEvents.txt" | ||
| f <- "C:/home/Development/matsim-org/matsim-episim/output-base/infectionEvents.txt" | ||
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| counts <- function(f) { | ||
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| data <- read.csv(f, sep = "\t") | ||
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| df <- table(data[[2]]) | ||
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| v <- as.numeric(df) | ||
|
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| # Persons who did not infect other persons | ||
| no_inf <- setdiff(data$infected, data$infector) | ||
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| res <- sort(c(rep(0, length(no_inf)), v)) | ||
| } | ||
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| est_disp <- function(f, mu=1) { | ||
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| matrix <- np$load(f) | ||
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| est <- list() | ||
| inf80 <- list() | ||
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| # Loaded vector is one row | ||
| v <- as.numeric(matrix) | ||
| #for(row in 1:nrow(matrix)) { | ||
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| #sprintf("Processing row %d\n", row) | ||
| #v <- matrix[row,] | ||
| v <- v[!is.na(v)] | ||
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| if (length(v) == 0) { | ||
| next | ||
| } | ||
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| fit <- try(fitdist(v, fix.arg=list(mu=mu), "nbinom"), silent = T) | ||
| if(inherits(fit, "try-error")) { | ||
| fit <- list(estimate=NaN) | ||
| } | ||
|
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| est <- c(est, as.numeric(fit$estimate)) | ||
|
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| total = sum(v) | ||
| s80 <- v[0:-length(v) * 0.8] | ||
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| inf80 <- c(inf80, sum(s80) * 100 / total) | ||
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| #} | ||
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| df <- data.frame(as.numeric(est), as.numeric(inf80)) | ||
| colnames(df) <- c("est", "top20") | ||
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| return(df) | ||
| } | ||
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| est_disp_zip <- function(f) { | ||
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| tmpdir <- tempdir() | ||
| unzip(f, exdir = tmpdir ) | ||
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| res <- data.frame() | ||
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| for (f in list.files(tmpdir, pattern = "*.npy", full.names = T)) { | ||
| df <- est_disp(f) | ||
| means <- colMeans(df, na.rm = T) | ||
| df <- data.frame(t(means)) | ||
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| row.names(df) <- c(basename(f)) | ||
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| res <- rbind(res, df) | ||
| } | ||
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| return(res) | ||
| } | ||
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| setwd("C:/Users/chris/Development/matsim-org/matsim-episim/biggest-jan") | ||
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| est_disp("0.46.npy") | ||
| est_disp("0.56.npy") | ||
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| # Testing the distribution | ||
| df <- sort(rnbinom(15000, size=1, mu=2.5)) | ||
| plot(hist(df, breaks = 30)) | ||
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| ggplot() + aes(df) + geom_histogram(binwidth=1, colour="black", fill="white", alpha=0.2) |
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