Merge branch 'feature/90_logging' into develop #92

Author: astrochun

Metallicity_Stack_Commons/__init__.py

@@ -5,18 +5,29 @@
 import getpass
 import numpy as np
 
+from .logging import log_stdout, log_verbose
+
 version = "1.2.0"
 
 lambda0   = [3726.18, 4101.73, 4340.46, 4363.21, 4861.32, 4958.91, 5006.84]
 line_type = ['Oxy2', 'Balmer', 'Balmer', 'Single', 'Balmer', 'Single', 'Single']
 line_name = ['OII_3727', 'HDELTA', 'HGAMMA', 'OIII_4363', 'HBETA', 'OIII_4958',
              'OIII_5007']
 
-line_name_short = {"OII": line_name[0], "4363": line_name[3],
-                   "HB": line_name[4], "OIII": line_name[-1],
-                   "HG": line_name[2], "HD": line_name[1]}
+line_name_short = {
+    "OII": line_name[0],
+    "4363": line_name[3],
+    "HB": line_name[4],
+    "OIII": line_name[-1],
+    "HG": line_name[2],
+    "HD": line_name[1]
+}
 
-fitting_lines_dict = {"lambda0": lambda0, "line_type": line_type, "line_name": line_name}
+fitting_lines_dict = {
+    "lambda0": lambda0,
+    "line_type": line_type,
+    "line_name": line_name
+}
 
 all_lambda0   = [lambda0[0]] + [3728.91] + lambda0[1:]
 all_line_name = ['OII_3726', 'OII_3729'] + line_name[1:]
@@ -40,70 +51,95 @@
 k_dict   = dict(zip(line_name, k_values))
 
 
-def exclude_outliers(objno):
+def exclude_outliers(objno, verbose=False, log=None):
     """
     Exclude spectra that are identified as outliers.
 
     Generally this is because the spectra have very high S/N on the continuum.
 
     :param objno: list or numpy array of eight-digit identifier
+    :param verbose: bool to write verbose message to stdout. Default: file only
+    :param log: LogClass or logging object
 
-    :return:
-     flag: numpy array of zeros and ones
+    :return flag: numpy array of zeros and ones
     """
 
+    if log is None:
+        log = log_stdout()
+
+    log_verbose(log, "starting ...", verbose=verbose)
+
     flag = np.zeros(len(objno), dtype=int)
-    bad_data = np.array(['32007727', '32101412', '42006031', '32035286', '14023705'])
+    bad_data = np.array(['32007727', '32101412', '42006031',
+                         '32035286', '14023705'])
     for ii in range(len(bad_data)):
-        idx = [xx for xx in range(len(objno)) if bad_data[ii] in str(objno[xx])]
+        idx = [xx for xx in range(len(objno)) if
+               bad_data[ii] in str(objno[xx])]
         flag[idx] = 1
 
+    log_verbose(log, "finished.", verbose=verbose)
     return flag
 
 
-def dir_date(folder_name, path_init='', year=False):
+def dir_date(folder_name, path_init='', year=False, verbose=False, log=None):
     """
     Purpose:
       This function finds and returns the path to a directory named after the
       current date (MMDDYYYY). If the directory doesn't exist yet, it creates
       a new directory named after the current date in the provided folder_name
       directory.
 
-        From https://github.com/rafia37/Evolution-of-Galaxies/blob/master/general.py
+      From https://github.com/rafia37/Evolution-of-Galaxies/blob/master/general.py
+
+    Usage:
+        fitspath = dir_date(folder_name, path_init='', year=True)
 
     :param folder_name: str containing directory for date subdirectory will be in
     :param path_init: root path. Default: empty string
     :param year: Indicate whether to include year in date folder. Default: False
+    :param verbose: bool to write verbose message to stdout. Default: file only
+    :param log: LogClass or logging object
 
-    :return: fitspath: Full path to the date directory
-
-    Usage:
-        fitspath = dir_date(folder_name, path_init='', year=True)
+    :return fitspath: Full path to the date directory
     """
 
+    if log is None:
+        log = log_stdout()
+
+    log_verbose(log, "starting ...", verbose=verbose)
+
     today = date.today()
 
-    list_path = [path_init, folder_name, "%02i%02i" % (today.month, today.day), '']
+    list_path = [path_init, folder_name,
+                 f"{today.month:02d}{today.day:02d}", '']
     if year:
-        list_path[-2] = "%i" % today.year + list_path[-2]
+        list_path[-2] = f"{today.year:d}" + list_path[-2]
 
     fitspath = os.path.join(*list_path)
     try:
         os.mkdir(fitspath)
     except FileExistsError:
-        print("Path already exists : ", fitspath)
+        log.warning(f"Path already exists : {fitspath}")
 
+    log_verbose(log, "finished.", verbose=verbose)
     return fitspath
 
 
-def get_user(username=None):
+def get_user(username=None, verbose=False, log=None):
+
+    if log is None:
+        log = log_stdout()
+
+    log_verbose(log, "starting ...", verbose=verbose)
 
-    if isinstance(username, type(None)):
+    if username is None:
         username = getpass.getuser()
 
     if username in fitspath_dict.keys():
         fitspath = fitspath_dict[username]
     else:
+        log.warning("Incorrect username input")
         raise ValueError("Incorrect username input")
 
+    log_verbose(log, "finished.", verbose=verbose)
     return fitspath

Metallicity_Stack_Commons/analysis/attenuation.py

@@ -1,6 +1,7 @@
 import numpy as np
 
 from .. import k_dict, line_name_short
+from ..logging import log_stdout, log_verbose
 
 from chun_codes import compute_onesig_pdf
 
@@ -18,27 +19,38 @@
 k_HDELTA = k_dict[HD]
 
 
-def compute_EBV(ratio, source='HgHb', zero_neg=True):
+def compute_EBV(ratio, source='HgHb', zero_neg=True, verbose=False, log=None):
     """
     Purpose:
       Determines E(B-V) from Hg/Hb or Hd/Hb flux ratios using Case B assumptions
 
     :param ratio: float or numpy array containing Hg/Hb or Hd/Hb
     :param source: str indicate ratio type.  Either 'HgHb' or 'HdHb'. Default: 'HgHb'
     :param zero_neg: boolean to indicate whether to zero out negative reddening. Default: True
+    :param verbose: bool to write verbose message to stdout. Default: file only
+    :param log: LogClass or logging object
 
     :return EBV: float or numpy array containing E(B-V).
                  Note: Not correcting for negative reddening
     :return EBV_peak: float or numpy array return when it is a 2-D distribution
     """
 
+    if log is None:
+        log = log_stdout()
+
+    log_verbose(log, "starting ...", verbose=verbose)
+
     if isinstance(ratio, list):
-        print("!!! Incorrect type for input [ratio].  Cannot be list !!!")
-        raise TypeError
+        log.warning("!!! Incorrect type for input [ratio].  Cannot be list !!!")
+        raise TypeError("!!! Incorrect type for input [ratio].  Cannot be list !!!")
+
+    if source not in ['HgHb', 'HdHb']:
+        log.warning("!!! Incorrect [source]")
+        raise KeyError("!!! Incorrect [source]")
 
-    if 'HgHb' in source:
-        ratio0 = HgHb_CaseB
-        k1 = k_HGAMMA
+    # For HgHb (default)
+    ratio0 = HgHb_CaseB
+    k1 = k_HGAMMA
 
     if 'HdHb' in source:
         ratio0 = HdHb_CaseB
@@ -50,58 +62,76 @@ def compute_EBV(ratio, source='HgHb', zero_neg=True):
         if isinstance(EBV, float):
             if EBV < 0.0:
                 EBV = 0.0
-                print('zero substituted for negative reddening')
+                log.info('zero substituted for negative reddening')
             return EBV
         else:
             if len(EBV.shape) == 1:
                 neg_idx = np.where(EBV < 0.0)[0]
                 if len(neg_idx) > 0:
                     EBV[neg_idx] = 0.0
-                    print('zero substituted for negative reddening')
+                    log.info('zero substituted for negative reddening')
+                log_verbose(log, "finished.", verbose=verbose)
                 return EBV
             if len(EBV.shape) == 2:
                 EBV_avg = np.average(EBV, axis=0)  # initial guess
                 EBV_err, EBV_peak = compute_onesig_pdf(EBV, EBV_avg, usepeak=True)
                 neg_idx = np.where(EBV_peak < 0.0)[0]
                 if len(neg_idx) > 0:
-                    print('EBV distribution shifted for peak')
+                    log.info('EBV distribution shifted for peak')
                     EBV[neg_idx, :] -= EBV_peak[neg_idx].reshape((len(neg_idx), 1))
                     EBV_peak[neg_idx] = 0.0
+                log_verbose(log, "finished.", verbose=verbose)
                 return EBV, EBV_peak
     else:
+        log_verbose(log, "finished.", verbose=verbose)
         return EBV
 
 
-def compute_A(EBV):
+def compute_A(EBV, verbose=False, log=None):
     """
     Purpose:
       Compute A(Lambda) for all possible emission lines
 
     :param EBV: float value of E(B-V)
       Has not been configured to handle a large array.  Some array handling would be needed
+    :param verbose: bool to write verbose message to stdout. Default: file only
+    :param log: LogClass or logging object
 
     :return A_dict: dict containing A(lambda) with keys identical to k_dict
     """
 
+    if log is None:
+        log = log_stdout()
+
+    log_verbose(log, "starting ...", verbose=verbose)
+
     k_arr  = np.array(list(k_dict.values()))
 
     A_arr  = k_arr * EBV
     A_dict = dict(zip(list(k_dict.keys()), A_arr))
 
+    log_verbose(log, "finished.", verbose=verbose)
     return A_dict
 
 
-def line_ratio_atten(ratio, EBV, wave_top, wave_bottom):
+def line_ratio_atten(ratio, EBV, wave_top, wave_bottom, verbose=False,
+                     log=None):
+
+    if log is None:
+        log = log_stdout()
+
+    log_verbose(log, "starting ...", verbose=verbose)
 
     k_top    = k_dict[wave_top]
     k_bottom = k_dict[wave_bottom]
 
     ratio_atten = ratio * 10**(0.4*EBV*(k_top - k_bottom))
 
+    log_verbose(log, "finished.", verbose=verbose)
     return ratio_atten
 
 
-def Hb_SFR(log_LHb, EBV):
+def Hb_SFR(log_LHb, EBV, verbose=False, log=None):
     """
     Purpose:
       Determine dust-corrected SFR using the H-beta luminosity and a
@@ -113,11 +143,19 @@ def Hb_SFR(log_LHb, EBV):
     :param log_LHb: numpy array or float containing logarithm of H-beta
            luminosity in units of erg/s
     :param EBV: numpy array or float providing E(B-V)
+    :param verbose: bool to write verbose message to stdout. Default: file only
+    :param log: LogClass or logging object
 
     :return logSFR: numpy array or float containing the SFR in
             logarithmic units of M_sun/yr
     """
 
+    if log is None:
+        log = log_stdout()
+
+    log_verbose(log, "starting ...", verbose=verbose)
+
     logSFR = np.log10(4.4e-42 * HaHb_CaseB) + 0.4*EBV*k_HBETA + log_LHb
 
+    log_verbose(log, "finished.", verbose=verbose)
     return logSFR