import somadata

# read the adat
my_adat = somadata.read_adat('/path/to/my/file1.adat')

# retrieve sample metadata
sample_meta = my_adat.index.to_frame(index=False)

# retrieve SOMAmer metadata
soma_meta = my_adat.columns.to_frame(index=False)

# retrieve the scale factors for all plates
plate_scale_factors_dict = my_adat.header_metadata['PlatformSpecificPlateScale_ScaleFactor']

### additional optional manipulation 

# concatenate multiple adats into a single adat
my_adat2 = somadata.read_adat('/path/to/my/file2.adat')
my_merged_adat = soma.smart_adat_concatenation([my_adat, my_adat2])

# write it to a file 
my_merged_adat = my_merged_adat.to_adat('/path/to/merged/adat')

library(SomaDataIO)

# check all package functions
ls("package:SomaDataIO")

#read the adat
my_adat <- read_adat('/path/to/my/file1.adat')

# retrieve sample metadata
sample_meta <- my_adat[getMeta(my_adat)]

# retrieve SOMAmer metadata
soma_meta <- my_adat[getAnalytes(my_adat)]

# create a function that parses the header
parse_adat_header <- function(adat_file, max_lines = 500) {
  header_lines <- readLines(adat_file, n = max_lines)
  table_row <- grep("TABLE_BEGIN", header_lines)
  header_lines[1:(table_row-1)] %>% 
    strsplit("\t") %>% 
    {
      values <- map(., 2) # Get values from key value pairs
      keys <- map_chr(., 1) # Get keys...
      setNames(values, keys)
    }
}

my_adat_header <- parse_adat_header('/path/to/my/file1.adat')

# retrieve the scale factors for all plates
plate_scale_factors_dict = my_adat_header$PlatformSpecificPlateScale_ScaleFactor

### additional optional manipulation 
my_adat2 = read_adat('/path/to/my/file2.adat')
my_merged_adat = rbind(my_adat, my_adat2)

# write it to a file 
my_merged_adat = write_adat(my_merged_adat, '/path/to/merged/adat')