## NOTE: The data is currently on /lts/mblab/downloaded_data/barkai_checseq
## and the parquet dataset is on the brentlab-strides aws at s3://yeast-binding-perturbation-data/barkai_checkseq

library(tidyverse)
library(here)
library(arrow)

# genomic feature harmonization table ----
# see https://huggingface.co/datasets/BrentLab/yeast_genome_resources
genomefeatures = arrow::open_dataset(here("data/genome_files/hf/features")) %>%
  as_tibble() %>%
  mutate(rownum = row_number())

chrmap = read_csv("~/projects/parsing_yeast_database_data/data/genome_files/chrmap.csv.gz")

# scp -r chasem@login.htcf.wustl.edu:/lts/mblab/downloaded_data/chipexo/paper_mimic_tag_count_20250718 .
genomecov_files = list.files(here("data/chip_exo/paper_mimic_tag_count_20250718"), full.names = TRUE)

genomecov_df = tibble(
  path = genomecov_files,
  tmp = str_remove(basename(genomecov_files), ".mLb.mkD.sorted_r1_5p_cov.txt")) %>%
  separate(tmp,c('sample', 'replicate'), extra = "merge", remove = FALSE) %>%
  mutate(replicate = ifelse(sample != "control",
                            as.integer(str_extract(str_extract(replicate, "REP\\d"), "\\d")),
                            as.integer(str_extract(str_extract(replicate, "T\\d+"), "\\d+"))))

nf_core_meta = read_csv(here("data/chip_exo/nfcore_chipseq_full_samplesheet.csv")) %>%
  group_by(sample) %>%
  mutate(tmp = row_number()) %>%
  ungroup() %>%
  mutate(replicate = ifelse(sample == "control", tmp, replicate)) %>%
  select(-tmp)

genomecov_df_meta = genomecov_df %>%
  left_join(nf_core_meta)

pugh_genomecov_tmp <- genomecov_df_meta %>%
  select(sample, replicate, run_accession, yeastepigenome_id, path) %>%
  mutate(sample = tolower(sample)) %>%
  left_join(
    genomefeatures %>%
      select(symbol, locus_tag) %>%
      distinct() %>%
      mutate(sample = tolower(symbol)),
    by = "sample"
  )

filled_missing_locus_tag =  pugh_genomecov_tmp %>%
  filter(!complete.cases(.)) %>%
  mutate(
    alias_rownum = map_int(sample, function(s) {
      idx <- which(str_detect(genomefeatures$alias, fixed(toupper(s))))
      if (length(idx) > 0) idx[1] else NA_integer_
    }),
    alias_rownum = ifelse(
      is.na(alias_rownum) & str_starts(sample, "y"),
      map_int(sample, function(s) {
        idx <- which(str_detect(genomefeatures$locus_tag, fixed(toupper(s))))
        if (length(idx) > 0) idx[1] else NA_integer_
      }),
      alias_rownum
    )
  ) %>%
  select(-c(symbol, locus_tag)) %>%
  left_join(genomefeatures %>%
              select(rownum, symbol, locus_tag) %>%
              dplyr::rename(alias_rownum=rownum)) %>%
  select(-alias_rownum)

pugh_genomecov_meta = pugh_genomecov_tmp %>%
  filter(!is.na(locus_tag)) %>%
  bind_rows(
    filled_missing_locus_tag) %>%
  mutate(regulator_symbol = symbol, regulator_locus_tag = locus_tag) %>%
    arrange(regulator_locus_tag) %>%
    group_by(regulator_locus_tag) %>%
    mutate(sample_id = cur_group_id())

# pugh_genomecov_meta %>%
#   select(regulator_locus_tag, regulator_symbol,
#          run_accession, yeastepigenome_id) %>%
# write_parquet("~/code/hf/rossi_2021/rossi_2021_metadata.parquet",
#               compression = "zstd",
#               write_statistics = TRUE,
#               use_dictionary = c(
#                 regulator_locus_tag = TRUE,
#                 regulator_symbol = TRUE
#               )
# )

process_chipexo_genomecov_file = function(covpath, accession_str){

  data.table::fread(covpath, sep = "\t", col.names=c('chr', 'pos', 'pileup')) %>%
    left_join(chrmap %>% select(refseq, ucsc) %>%
                dplyr::rename(chr=refseq)) %>%
    mutate(chr = ucsc,
           accession = accession_str) %>%
    select(chr, pos, pileup, accession)

}


# Output base directory for partitioned dataset
output_parquet_dir = file.path(here("data/chip_exo/"), "genome_map")
dir.create(output_parquet_dir)

# Write incrementally
# for (accession_str in pugh_genomecov_meta$run_accession) {
#
#   sample = pugh_genomecov_meta %>% filter(run_accession == accession_str) %>% pull(sample)
#   replicate_str = pugh_genomecov_meta %>% filter(run_accession == accession_str) %>% pull(replicate)
#   path = pugh_genomecov_meta %>% filter(run_accession == accession_str) %>% pull(path)
#
#
#     message(glue::glue("Processing {sample} ({replicate_str})"))
#
#     df <- process_chipexo_genomecov_file(
#       path,
#       accession_str
#     )
#
#     # Write just this sample's data to the appropriate partition
#     arrow::write_dataset(
#       df,
#       path = output_parquet_dir,
#       format = "parquet",
#       partitioning = c("accession"),
#       existing_data_behavior = "overwrite",
#       compression = "zstd",
#       write_statistics = TRUE,
#       use_dictionary = c(
#         chr = TRUE
#       )
#     )
# }