tests/haldane_burn_local_test.R

In eruud/test.calorimeter.r: Calorimeter processing functions

# Load necessary packages
library(pilr.api.r)
library(uuid)
library(dplyr)
library(jsonlite)
library(base64enc)

# Set options for server, project, access code
options(pilr_server_default = "http://qa.pilrhealth.com")
options(pilr_project_default = "infusiontest-4-1-17")
options(pilr_default_access_code = "e3c682d7-2689-40ac-82e2-a0e29b4793ff")
participant = "burn"

# Retrieve data (can append more datasets to the list if workunit requires them)
data <- list(calrq = read_pilr(data_set = "pilrhealth:calrq:calrq_data", schema = "1", 
                               query_params = list(participant = participant)))

# Set your params (participant variables and instrument settings)
params = list(settings = read_pilr_params(data_set = "pilrhealth:calrq:calrq_data", schema = "1", 
                                          instrument = "calrq", participant = participant, period = "active_period"))
params$package <- "test.calorimeter.r"

# Fix N2 array
multiple_n2 <- eval(parse("", n=NULL, gsub('\n', "", params$settings$multiple_n2$value, fixed = TRUE)))
fields = list() 

for (i in 1:length(multiple_n2$fields))
{
  fields[i] = list(as.data.frame(multiple_n2$fields[[i]]))
  fields[[i]]$value = list(as.numeric(multiple_n2$fields[[i]]$value[1]),
                           list('$date' = multiple_n2$fields[[i]]$value[2]),multiple_n2$fields[[i]]$value[3])
  print(i)
}
end

json_frame = data.frame(matrix(1,2))
json_frame$fields = fields
json_frame <- subset(json_frame, select = -1)
params$settings$multiple_n2$value <- toJSON(json_frame)

# Remap variables with changed names (set in job def JSON)
params$settings$volume <- params$settings$chamber_volume
params$settings$CO2_MFC <- params$settings$co2_mfc
params$settings$N2_MFC <- params$settings$n2_mfc
params$settings$gas_type$value <- gsub(' ', '', paste("[",params$settings$gas_type$value,"]"))
params$settings$gas <- params$settings$gas_type

# Run haldane transform
data$haldane <- apply_haldane(data, params)

# Get data for human summary
data$event_tags <- list(calrq = read_pilr(data_set = "pilrhealth:time_analysis:tagged_event", schema = "1", 
                                          query_params = list(participant = participant)))
data$event_tags <- data$event_tags$calrq

# Run human summary
ret <- process_cal_burn(data, params)