docs: def iodp_samples_srm(df, spec_file='specimens.txt',samp_file="samples.txt",site_file="sites.txt",dir_path='.',
                   input_dir_path='',comp_depth_key="",lat="",lon=""):
      """
      Convert IODP samples data generated from the SRM measurements file into datamodel 3.0 MagIC samples file.
      Default is to overwrite the output files in your working directory.
  
      Parameters
      ----------
      df : str
          Pandas DataFrame of SRM Archive data
      dir_path : str
          working directory, default "."
      input_dir_path : str
          input file directory IF different from dir_path, default ""
  
      spec_file : str
          output specimens.txt file name
      samp_file : str
          output samples.txt file name
      site_file : str
          output sites.txt file name
      comp_depth_key : str
          if not "", there is a composite depth model, for example 'Depth CSF-B (m)'
      lat : float
          latitude of hole location
      lon : float
          longitude of hole location
  
      NOTE:  all output files will overwrite existing files.
  
      Returns
      --------
      type - Tuple : (True or False indicating if conversion was sucessful,file names written)
  
      """
      # define required columns for samples, sites, locations (holes).
      spec_reqd_columns=['specimen','sample','result_quality','method_codes','volume',\
                         'specimen_name_alternatives','citations']
      samp_reqd_columns=['sample','site','result_type','result_quality',\
                         'azimuth','dip','azimuth_correction','method_codes','citations','core_depth','composite_depth']
      site_reqd_columns=['site','location','lat','lon','result_type','result_quality','method_codes',\
                         'core_depth','composite_depth',\
                         'geologic_types','geologic_classes','geologic_lithologies',\
                        'age','age_low','age_high','age_unit','citations']
      # fix the path names for input and output directories (if different)
      input_dir_path, output_dir_path = pmag.fix_directories(input_dir_path, dir_path)
      # find the input sample file name downloaded from LORE
      # set the output file names
      spec_out = os.path.join(output_dir_path, spec_file)
      samp_out = os.path.join(output_dir_path, samp_file)
      site_out = os.path.join(output_dir_path, site_file)
      # read in the data from the downloaded .csv file
      # create the MagIC data model 3.0 DataFrames with the required column
      specimens_df=pd.DataFrame(columns = spec_reqd_columns)
      samples_df=pd.DataFrame(columns = samp_reqd_columns)
      sites_df=pd.DataFrame(columns = site_reqd_columns)
      # set some column headers for the sample master .csv file
      depth_key = "Depth CSF-A (m)"
      text_key = "Text ID"
      date_key = "Date sample logged"
      volume_key = 'Volume (cm3)'
      # convert the meta data in the sample master to MagIC datamodel 3
      # construct the sample name
  
      holes,specimens=iodp_sample_names(df)
      # put the sample name in the specimen, sample, site
      specimens_df['specimen']=specimens
      specimens_df['sample']=specimens
      samples_df['sample']=specimens
      samples_df['site']=specimens
      samples_df['core_depth']=df[depth_key]
      if comp_depth_key: samples_df['composite_depth']=df[comp_depth_key]
  
      # add in the rest to the sites table
      sites_df['site']=specimens
      sites_df['core_depth']=df[depth_key]
      sites_df['lat']=lat
      sites_df['lon']=lon
      sites_df['result_type']='i'
      sites_df['result_quality']='g'
      sites_df['method_codes']='FS-C-DRILL-IODP'
      sites_df['location']=holes[0]
      sites_df['citations']="This study"
      if comp_depth_key: sites_df['composite_depth']=df[comp_depth_key]
      # now do the samples
  
      samples_df['method_codes']='FS-C-DRILL-IODP:SP-SS-C:SO-V' # could put in sampling tool...
      samples_df['site']=specimens # same as sample and specimen name
      samples_df['result_type']='i'
      samples_df['result_type']='g'
      samples_df['azimuth']='0'
      samples_df['dip']='0'
      samples_df['citations']='This study'
  
      # NEED TO ADD TIMESTAMP FROM TIMESTAMP KEY SOMEDAY
      # and the specimens
      specimens_df['result_quality']='g'
      specimens_df['method_codes']='FS-C-DRILL-IODP'
      specimens_df['citations']='This study'
      # fill in the np.nan with blanks
      specimens_df.fillna("",inplace=True)
      samples_df.fillna("",inplace=True)
      sites_df.fillna("",inplace=True)
      # save the files in the designated spots spec_out, samp_out, site_out and loc_out
      spec_dicts = specimens_df.to_dict('records')
      pmag.magic_write(spec_out, spec_dicts, 'specimens')
      samp_dicts = samples_df.to_dict('records')
      pmag.magic_write(samp_out, samp_dicts, 'samples')
      site_dicts = sites_df.to_dict('records')
      pmag.magic_write(site_out, site_dicts, 'sites')
  
      return holes[0],specimens