Metallicity_Stack_Commons.column_names module¶

Metallicity_Stack_Commons.column_names.indv_M_LHb()¶

Use remove_from_list() to provide simplified list that contains ID, logM and logLHb

Return type: list
Returns: List containing just ID, logM, logLHb

Metallicity_Stack_Commons.column_names.indv_R23_O32()¶

Use remove_from_list() to provide simplified list that contains ID, logR23 and logO32

Return type: list
Returns: List containing just ID, logR23, logO32

Metallicity_Stack_Commons.column_names.line_fit_suffix_add(line_name0, line_type0)¶

Simple list comprehension combining emission line fit suffixes with the emission line. This works for individual lines

Parameters

line_name0 (str) – Line name
line_type0 (str) – Emission-line type (e.g., ‘Balmer’)

Return type

list

Returns

List of strings formatted as [LINE]_[SUFFIX]

Metallicity_Stack_Commons.column_names.merge_column_names(*args)¶

Merges multiple lists containing column names.

Usage:: column_names = merge_column_names(bin_names0, indv_names0)

Parameters: args (list) – An undefined number of lists
Return type: list
Returns: Merged list

Metallicity_Stack_Commons.column_names.remove_from_list(list0, remove_entries)¶

Purpose:: Remove entries from list of column names

Parameters

list0 (list) – List of column names
remove_entries (list) – List of column names to remove

Return type

list

Returns

List of column names after removal

Metallicity_Stack_Commons.logging module¶

class Metallicity_Stack_Commons.logging.LogClass(log_dir, logfile)¶

Bases: object

Main class to log information to stdout and ASCII logfile

Note: This code is identical to the one used in ReQUIAM:: https://github.com/ualibraries/ReQUIAM
To use:: log = LogClass(log_dir, logfile).get_logger()

Parameters

log_dir (str) – Relative path for exported logfile directory
logfile (str) – Filename for exported log file

get_logger()¶

Return type: Logger

Metallicity_Stack_Commons.logging.get_user_hostname()¶

Retrieve user, hostname, IP, and OS configuration

Return type: dict
Returns: Dictionary with ‘user’ ‘hostname’ and ‘ip’ keys

Metallicity_Stack_Commons.logging.log_stdout()¶

Returns stdout logging object

Return type: Logger

Metallicity_Stack_Commons.logging.log_verbose(log, message, verbose=False)¶

Log message depending on verbosity

Parameters

log (Logger) – logging.Logger object
message (str) – Message
verbose (bool) – Write verbose message to stdout. Default: file only

Metallicity_Stack_Commons.update_det4363_info module¶

Metallicity_Stack_Commons.update_det4363_info.get_index(det4363_table, input_table, column_name, verbose=False, log=<Logger stdout_logger (INFO)>)¶

Uses either OBJNO or AP/SLIT info to get index for an existing table

Parameters

det4363_table (Table) – Astropy table containing DEEP2 [OIII]4363-detected sample
input_table (Table) – Astropy table containing the entire sample to be updated
column_name (str) – Column name for cross-matching
verbose (bool) – Write verbose message to stdout. Default: file only
log (Logger) – logging.Logger object

Return type

Tuple[ndarray, ndarray]

Returns

Index arrays for det4363_table, input_table

Metallicity_Stack_Commons.valid_table module¶

Metallicity_Stack_Commons.valid_table.compare_to_by_eye(fitspath, dataset)¶

This function takes the automated validation table and checks it against inputted measurement that are determined by eye. These inputted measurements are in the np.where statements. It outputs a revised validation table based on the inputted measurements.

Usage:: valid_table.make_validation_table(fitspath, dataset)

Parameters

fitspath (str) – Full file path where the input file is and where the output file will be placed.
dataset (str) – Determine which eye measurements to use

Outputs:

fitspath + ‘bin_validation_revised.tbl’ and ‘.csv’: Validation table containing bin IDs; number of galaxies in each bin; and column indicating OIII4363 detection/non-detection, OIII4363_Flux_Observed, OIII4363_S/N, Notes

Metallicity_Stack_Commons.valid_table.make_validation_table(fitspath)¶

This function creates a validation table for a given binning set. The validation table contains a OIII4363 detection column where 1.0 means detection, 0.5 means non-detection with reliable OIII5007, and 0.0 means unreliable non-detection. This function will be run every time the analysis is completed and will create a validation table for every analysis.

Usage:: valid_table.make_validation_table(fitspath, bin_type_str)

Parameters: fitspath (str) – Full file path where the input file is and where the output file will be placed.

Outputs:

fitspath + ‘bin_validation.tbl’: Validation table containing bin IDs; number of galaxies in each bin; and column indicating OIII4363 detection/non-detection, OIII4363_Flux_Observed, OIII4363_S/N