generate_metadata_archive/generate_metadata_9003.R

In NiklasTR/dcp_helper: Create metadata and job files for distributed cell profiler

################ Important Inputs
## Define directory names - you will have to change this manually!
## The pipeline assumes there is a directory in the "inbox" directory that has the following name:
## Call this script with: Rscript generate_metadata_"your-name-here".R
plate_name = c("000012049003__2019-01-29T19_11_28-Measurement_3",
               "000012049003__2019-01-31T18_40_09-Measurement_1",
               "000012049003__2019-02-01T12_09_07-Measurement_4",
               "000012049003__2019-01-28T21_02_07-Measurement_2") # added later on for feature grouping

################ Sometimes you also have to change these variables
## json templates
new_json_path_flat = "/home/ubuntu/bucket/metadata/job_flatfield_template.json"
new_json_path_max = "/home/ubuntu/bucket/metadata/job_maxproj_template.json"
new_json_path_seg = "/home/ubuntu/bucket/metadata/job_segmentation_template.json"

## Name channels
channel_v <- c("ch1", "ch2", "ch3", "ch4")
channel_n <- c("pc", "bf", "ce", "tm")

################ This is where the execution starts
################ Creating metadata
library(tidyverse)
library(dcphelper)

## Define paths
new_path_base = paste0("/home/ubuntu/bucket/metadata/", plate_name,"/")
inbox_path_base= paste0("/home/ubuntu/bucket/inbox/", plate_name,"/Images/")
flatfield_path_base= paste0("/home/ubuntu/bucket/flatfield/", plate_name,"/")

# ## Creating target dir
# lapply(new_path_base, dir.create) # Do not execute this from a local machine if you expect other AWS services to access the directory later on
#
# ## Creating metadata directories
# print("creating pc metadata")
# for(j in 1:length(inbox_path_base)){
#   metadata_split_path <- create_flatfield_metadata_split(
#     path = inbox_path_base[j],
#     channel_of_interest = channel_v[1], #brightfield
#     name = "pc",
#     json_path = new_json_path, #not needed
#     path_base = new_path_base[j],
#     force = FALSE,
#     include_brightfield_proj = TRUE,
#     include_additional_proj = TRUE)
# }


# for(i in 2){
#   print(paste0("creating ", channel_n[i], " metadata"))
#   for(j in 1:length(inbox_path_base)){
#     metadata_split_path <- create_flatfield_metadata_split(
#       path = inbox_path_base[j],
#       channel_of_interest = channel_v[i], #brightfield
#       name = channel_n[i],
#       json_path = new_json_path, #not needed
#       path_base = new_path_base[j],
#       force = FALSE)
#   }
# }

################ Grouping data
#
# print("Creating shell script for grouping")
# generate_group(plate_name, channel_n)
#
# print("Grouping data using python script")
# path = paste0("/home/ubuntu/bucket/metadata/", plate_name[1], "/", plate_name[1], "_create_group.sh")
# system(path)
#
# ################ Aggregating information and executable file
#
# for(j in new_path_base){
#   link_json_metadata(metadata_split_path = list.files(j, pattern = "metadata_", full.names = TRUE) %>%
#                        stringr::str_subset(pattern = ".csv") %>%
#                        stringr::str_subset(pattern = "pc"),
#                      json_path = new_json_path_seg,
#                      path_base = j)
# }
#
#
# for(j in new_path_base){
#   link_json_metadata(metadata_split_path = list.files(j, pattern = "metadata_", full.names = TRUE) %>%
#                        stringr::str_subset(pattern = ".csv") %>%
#                        stringr::str_subset(pattern = "bf"),
#                      json_path = new_json_path_flat,
#                      path_base = j)
# }
#
#
# channel_n_mod <- channel_n[3:4]
# for(j in new_path_base){
#   for(i in 1:length(channel_n_mod)){
#     link_json_metadata(metadata_split_path = list.files(j, pattern = "metadata_", full.names = TRUE) %>%
#                          stringr::str_subset(pattern = ".csv") %>%
#                          stringr::str_subset(pattern = channel_n_mod[i]),
#                        json_path = new_json_path_max,
#                        path_base = j)
#   }
# }
#
# ### Grouping final job files
#
# for(j in new_path_base){
#   for(i in 1:length(channel_n)){
#     group_jobs_bash(path_base = j,
#                     name = channel_n[i],
#                     letter_row_interval = c(1:16),
#                     number_col_interval = c(1:24))
#   }
# }

# collecting features after processing
for(i in flatfield_path_base){
  collect_feature_data_db(i)
}