diff --git a/bgcArgoDMQC.egg-info/PKG-INFO b/bgcArgoDMQC.egg-info/PKG-INFO
index 1cc91ec..232dd4e 100644
--- a/bgcArgoDMQC.egg-info/PKG-INFO
+++ b/bgcArgoDMQC.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: bgcArgoDMQC
-Version: 0.2.10
+Version: 0.2.11
 Summary: A python library for quality control of BGC-Argo data
 Home-page: https://github.com/ArgoCanada/bgcArgoDMQC
 Author-email: chris.gordon@dfo-mpo.gc.ca
@@ -45,10 +45,10 @@ Description: # Argo Canada BGC Quality Control
         ## bgcArgo dependencies
         
         - Must run on `python3.4` or higher, not supported on `python2.x` (uses [pathlib](https://docs.python.org/3/library/pathlib.html), introduced in python version 3.4)
-        - TEOS-10 package [gsw](https://teos-10.github.io/GSW-Python/), but will also work with the [seawater](https://pypi.org/project/seawater/) package, though it is deprecated in favor of gsw
+        - TEOS-10 package [gsw](https://teos-10.github.io/GSW-Python/)
         - [netCDF4](https://pypi.org/project/netCDF4/) module for `.nc` files
-        - [pandas](https://pandas.pydata.org/) is required (and highly recommended for all your data science needs!)
-        - [seaborn](https://seaborn.pydata.org/) is recommended but not required, through there will be some reduced (non-essential) functionality
+        - [pandas](https://pandas.pydata.org/) is required
+        - [seaborn](https://seaborn.pydata.org/)
         - [cmocean](https://matplotlib.org/cmocean/) is also recommended for nicer plots, but not required
         
         ## basic functionality
diff --git a/bgcArgoDMQC.egg-info/SOURCES.txt b/bgcArgoDMQC.egg-info/SOURCES.txt
index e529563..54e41e9 100644
--- a/bgcArgoDMQC.egg-info/SOURCES.txt
+++ b/bgcArgoDMQC.egg-info/SOURCES.txt
@@ -4,11 +4,13 @@ README.md
 requirements.txt
 setup.py
 bgcArgoDMQC/__init__.py
+bgcArgoDMQC/configure.py
 bgcArgoDMQC/core.py
 bgcArgoDMQC/diagnostic.py
 bgcArgoDMQC/fplt.py
 bgcArgoDMQC/interp.py
 bgcArgoDMQC/io.py
+bgcArgoDMQC/lut.py
 bgcArgoDMQC/unit.py
 bgcArgoDMQC/util.py
 bgcArgoDMQC.egg-info/PKG-INFO
diff --git a/bgcArgoDMQC/meta.yaml b/bgcArgoDMQC/meta.yaml
index 89fef53..9340f1f 100644
--- a/bgcArgoDMQC/meta.yaml
+++ b/bgcArgoDMQC/meta.yaml
@@ -1,5 +1,5 @@
 {% set name = "bgcArgoDMQC" %}
-{% set version = "0.2.10" %}
+{% set version = "0.2.11" %}
 
 
 package:
@@ -8,7 +8,7 @@ package:
 
 source:
   url: https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/bgcArgoDMQC-{{ version }}.tar.gz
-  sha256: ba2047e3291b180a6b0749e83575c88eacaafe8e05d6281e4340138805857364
+  sha256: 9e69abd3e75c3e673c7a31ace9b3e72401ea5b478475dba211705936562fab53
 
 build:
   number: 0
diff --git a/build/lib/bgcArgoDMQC/__init__.py b/build/lib/bgcArgoDMQC/__init__.py
index 336a6ff..db27280 100644
--- a/build/lib/bgcArgoDMQC/__init__.py
+++ b/build/lib/bgcArgoDMQC/__init__.py
@@ -28,6 +28,10 @@
 '''
 
 from __future__ import absolute_import
+
+from . import configure
+configure.check_config()
+
 from .core import *
 from . import fplt
 from . import unit
@@ -36,7 +40,7 @@
 from . import interp
 from . import diagnostic
 
-__all__ = ['fplt', 'unit', 'util', 'io', 'interp', 'diagnostic']
+__all__ = ['fplt', 'unit', 'util', 'io', 'interp', 'diagnostic', 'configure']
 
 __author__ = ['Christopher Gordon <chris.gordon@dfo-mpo.gc.ca>']
 
@@ -45,3 +49,11 @@
 # check age of index file, or if it exists
 if not io.index_exists():
     io.update_index()
+
+# get a dict with with config info
+config = configure.read_config()
+# set the directories within the config file
+dir_config = {k:v for k,v in config.items() if k in ['argo_path', 'woa_path', 'ncep_path']}
+set_dirs(**dir_config)
+
+from .core import *
\ No newline at end of file
diff --git a/build/lib/bgcArgoDMQC/configure.py b/build/lib/bgcArgoDMQC/configure.py
new file mode 100644
index 0000000..1b7b328
--- /dev/null
+++ b/build/lib/bgcArgoDMQC/configure.py
@@ -0,0 +1,86 @@
+from pathlib import Path
+
+global config_file
+pkg_path = Path(__file__).parent.absolute()
+config_file = pkg_path / '.config'
+
+def check_config():
+    '''
+    Check that configuration file exists, and if not, create a blank one. 
+    Having a blank file exist eases throwing any FileNotFoundError's, but
+    if the config is blank then still no configuration is performed.
+    '''
+    # make sure its there, even as an empty file, eases handling of FileNotFoundError's
+    if not config_file.exists():
+        config_file.touch()
+
+def read_config():
+    '''
+    Read in permanently stored configuration information for locations of
+    Argo, NCEP, or WOA data.
+    '''
+    # dict object to load values into
+    config_dict = dict()
+    # loop through the config file - if there are no lines, will return an
+    # empty dict, which is fine/right
+    with open(config_file) as f:
+        for line in f:
+            llist = line.split('=')
+            key = llist[0]
+            val = llist[1].strip()
+            config_dict[key] = val
+    
+    return config_dict
+
+def reset_config():
+    '''
+    Permanently erase .config data, replace with empty file.
+    '''
+    config_file.unlink()
+    config_file.touch()
+
+def configure(argo_path=None, ncep_path=None, woa_path=None, operator_name=None, operator_orcid=None, default_url=None):
+    '''
+    Set up locations for Argo, NCEP, and/or WOA data on local machine.
+    '''
+    # if they aren't already paths, make them paths, then make posix
+    if argo_path is not None:
+        argo_path = Path(argo_path).as_posix()
+    if ncep_path is not None:
+        ncep_path = Path(ncep_path).as_posix()
+    if woa_path is not None:
+        woa_path = Path(woa_path).as_posix()
+
+    # existing and new configuration info
+    existing_config = read_config()
+    new_config = dict(
+        argo_path=argo_path, ncep_path=ncep_path, woa_path=woa_path,
+        operator_name=operator_name, operator_orcid=operator_orcid,
+        default_url=default_url
+    )
+
+    # get rid of None values
+    clean_new_config = {k: v for k, v in new_config.items() if v is not None}
+
+    # config info already in .config file that should be kept
+    keep_config = set(existing_config.keys()) - set(clean_new_config.keys())
+
+    # config info that either exists in .config and should be overwritten, or is new to .config
+    overwritten_config = set(existing_config.keys()).intersection(set(clean_new_config.keys()))
+    add_config = set(clean_new_config.keys()) - set(existing_config.keys())
+    write_config = overwritten_config | add_config
+
+    final_config = dict()
+    for k in keep_config:
+        final_config[k] = existing_config[k]
+    for k in write_config:
+        final_config[k] = clean_new_config[k]
+
+    # write the .config file anew
+    with open(config_file, 'w') as f:
+        i = 0
+        for k, v in final_config.items():
+            if i > 0:
+                f.write('\n')
+            f.write('{}={}'.format(k, v))
+            i += 1
\ No newline at end of file
diff --git a/build/lib/bgcArgoDMQC/core.py b/build/lib/bgcArgoDMQC/core.py
index 7b8d2a6..6041b0c 100644
--- a/build/lib/bgcArgoDMQC/core.py
+++ b/build/lib/bgcArgoDMQC/core.py
@@ -12,12 +12,7 @@
 from matplotlib.offsetbox import AnchoredText
 import datetime
 
-# try to import seaborn
-try:
-    sns_flag = True
-    import seaborn as sns
-except:
-    sns_flag = False
+import seaborn as sns
 
 # try to import cartopy
 try:
@@ -27,19 +22,7 @@
 except:
     map_flag = False
 
-# soft attempt to load gsw, but allow for seawater as well
-try: 
-    import gsw
-    flagSA = True
-except:
-    try:
-        # if this also fails, just load gsw to throw the error
-        from seawater import pden
-        flagSA = False
-        warnings.warn('gsw package for thermodynamic equations of seawater not installed, attempting to load seawater package, however seawater is deprecated in favour of gsw-python, see https://teos-10.github.io/GSW-Python/\n')
-    except:
-        import gsw
-
+import gsw
 from netCDF4 import Dataset
 
 from . import io
@@ -47,27 +30,39 @@
 from . import unit
 from . import util
 from . import fplt
+from . import configure
 
 # ----------------------------------------------------------------------------
 # LOCAL MACHINE SETUP
 # ----------------------------------------------------------------------------
 
-ARGO_PATH = './'
-WOA_PATH  = None
-NCEP_PATH = None
-
-__bgcindex__ = io.read_index()
 global REF_PATH
 REF_PATH = Path(__file__).parent.absolute() / 'ref'
 
-def set_dirs(argo_path=ARGO_PATH, woa_path=WOA_PATH, ncep_path=NCEP_PATH):
+def get_config_dirs():
+    '''
+    Get previously set local directories to look for Argo, WOA, and NCEP data.
+    '''
+
+    config = configure.read_config()
+    if 'argo_path' in config.keys():
+        global ARGO_PATH
+        ARGO_PATH = config['argo_path']
+    if 'ncep_path' in config.keys():
+        global NCEP_PATH
+        NCEP_PATH = config['ncep_path']
+    if 'woa_path' in config.keys():
+        global WOA_PATH
+        WOA_PATH = config['woa_path']
+
+def set_dirs(argo_path='./', woa_path=None, ncep_path=None):
     '''
     Set local directories to look for Argo, WOA, and NCEP data.
 
     Args:
         argo_path (str or path-like): location of local Argo data
-        woa_path (str or path-like): location of local World Ocean Atlas data
         ncep_data (str or path-like): location of local NCEP data
+        woa_path (str or path-like): location of local World Ocean Atlas data
     '''
 
     global ARGO_PATH
@@ -77,7 +72,7 @@ def set_dirs(argo_path=ARGO_PATH, woa_path=WOA_PATH, ncep_path=NCEP_PATH):
     global NCEP_PATH
     NCEP_PATH = ncep_path
 
-def get_index(index='bgc'):
+def get_index(index='bgc', **kwargs):
     '''
     Get the global, biogeochemical, synthetic, or metadata Argo index. 
 
@@ -85,29 +80,62 @@ def get_index(index='bgc'):
         index (str): *bgc* for the biogeochemical Argo index, *global* for the core index, *synthetic* for the synthetic index, or *meta* for the metadata index
     '''
     if index == 'bgc':
-        return __bgcindex__
+        if '__bgcindex__' not in globals():
+            global __bgcindex__
+            __bgcindex__ = io.read_index()
+        return_index = __bgcindex__
     elif index == 'global':
         if '__globalindex__' not in globals():
             global __globalindex__
             __globalindex__ = io.read_index(mission='C')
-        return __globalindex__
+        return_index = __globalindex__
     elif index == 'synthetic':
         if '__synthindex__' not in globals():
             global __synthindex__
             __synthindex__ = io.read_index(mission='S')
-        return __synthindex__
-    elif index == 'synthetic':
-        if '__synthindex__' not in globals():
+        return_index = __synthindex__
+    elif index == 'meta':
+        if '__metaindex__' not in globals():
             global __metaindex__
             __metaindex__ = io.read_index(mission='M')
-        return __metaindex__
+        return_index = __metaindex__
+    elif index == 'traj':
+        if '__trajindex__' not in globals():
+            global __trajindex__
+            __trajindex__ = io.read_index(mission='T')
+        return_index = __trajindex__
     else:
         raise ValueError('Input "{}" is unrecognized'.format(index))
 
+    for arg, val in kwargs.items():
+        return_index = return_index[return_index[arg] == val]
+    
+    return return_index.reset_index()
+
+
 # ----------------------------------------------------------------------------
 # FLOAT CLASS
 # ----------------------------------------------------------------------------
 
+class traj:
+    '''
+    Class that loads Argo trajectory file data for a given float ID number
+    (wmo).
+    '''
+
+    def __init__(self, wmo, keep_fillvalue=False, verbose=False):
+
+        self.__trajdict__, self.__trajfile__ = load_traj(ARGO_PATH, wmo, verbose=verbose)
+
+        # local path info
+        self.argo_path = ARGO_PATH
+        self.woa_path  = WOA_PATH
+        self.ncep_path = NCEP_PATH
+
+        if not keep_fillvalue:
+            self.rm_fillvalue()
+
+
 class sprof:
     '''
     Class that loads Argo synthetic profile data for a given float ID number
@@ -132,9 +160,9 @@ class sprof:
     
     set_dirs = set_dirs
 
-    def __init__(self, wmo, keep_fillvalue=False, rcheck=True):
+    def __init__(self, wmo, keep_fillvalue=False, rcheck=True, verbose=False):
 
-        self.__floatdict__, self.__Sprof__, self.__BRtraj__, self.__meta__ = load_argo(ARGO_PATH, wmo, grid=True)
+        self.__floatdict__, self.__Sprof__, self.__BRtraj__, self.__meta__ = load_argo(ARGO_PATH, wmo, grid=True, verbose=verbose)
         self.__rawfloatdict__ = self.__floatdict__
 
         # local path info
@@ -156,9 +184,8 @@ def assign(self, floatdict):
         '''
 
         # metadata and dimension variables
-        self.floatName  = floatdict['floatName']
         self.floatType  = floatdict['floatType']
-        self.N_CYCLES   = floatdict['N_CYCLES']
+        self.N_PROF     = floatdict['N_PROF']
         self.N_LEVELS   = floatdict['N_LEVELS']
         self.CYCLE      = floatdict['CYCLES']
         self.CYCLE_GRID = floatdict['CYCLE_GRID']
@@ -181,10 +208,7 @@ def assign(self, floatdict):
         self.PSAL    = floatdict['PSAL']
         self.PSAL_QC = floatdict['PSAL_QC']
         # potential density
-        if flagSA:
-            self.PDEN = gsw.pot_rho_t_exact(gsw.SA_from_SP(self.PSAL, self.PRES, self.LONGITUDE_GRID, self.LATITUDE_GRID), self.TEMP, self.PRES, 0) - 1000
-        else:
-            self.PDEN = pden(self.PSAL, self.TEMP, self.PRES, 0) - 1000
+        self.PDEN = gsw.pot_rho_t_exact(gsw.SA_from_SP(self.PSAL, self.PRES, self.LONGITUDE_GRID, self.LATITUDE_GRID), self.TEMP, self.PRES, 0) - 1000
 
         # bgc variables - not necessarily all there so check if the fields exist
         if 'DOXY' in floatdict.keys():
@@ -218,6 +242,17 @@ def assign(self, floatdict):
             self.O2Sat = floatdict['O2Sat']
             self.O2Sat_QC = floatdict['O2Sat_QC']
 
+    def get_gridded_var(self, *args):
+
+        if not hasattr(self, '__griddict__'):
+            self.__griddict__ = read_sprof_gridded_variables(Dataset())
+
+        varlist = list()
+        for v in args:
+            varlist.append(self.__griddict__[v])
+
+        return varlist
+
     def rm_fillvalue(self):
         '''
         Remove FillValue from all variables.
@@ -225,6 +260,7 @@ def rm_fillvalue(self):
         self.__nofillvaluefloatdict__ = dict_fillvalue_clean(self.__rawfloatdict__)
         self.__floatdict__ = copy.deepcopy(self.__nofillvaluefloatdict__)
         self.assign(self.__nofillvaluefloatdict__)
+        self.to_dataframe()
 
     def clean(self, bad_flags=None):
         '''
@@ -235,6 +271,7 @@ def clean(self, bad_flags=None):
         self.__cleanfloatdict__ = dict_clean(self.__floatdict__, bad_flags=bad_flags)
         self.__floatdict__ = copy.deepcopy(self.__cleanfloatdict__)
         self.assign(self.__cleanfloatdict__)
+        self.to_dataframe()
 
     def reset(self):
         '''
@@ -243,8 +280,9 @@ def reset(self):
         '''
         self.__floatdict__ = copy.deepcopy(self.__rawfloatdict__)
         self.assign(self.__rawfloatdict__)
+        self.to_dataframe()
 
-    def check_range(self, key):
+    def check_range(self, key, verbose=False):
         '''
         Performs a range check for variables that have a RTQC range available.
         Replaces values outside the range with NaN values. Takes string input
@@ -259,14 +297,17 @@ def check_range(self, key):
             key = [key]
         
         for k in key:
-            self.__rangecheckdict__ = range_check(k, self.__floatdict__)
-            self.__floatdict__ = self.__rangecheckdict__
+            if k in self.__floatdict__.keys():
+                self.__rangecheckdict__ = range_check(k, self.__floatdict__, verbose=verbose)
+                self.__floatdict__ = self.__rangecheckdict__
 
-            # recalculate O2sat if its DOXY
-            if k == 'DOXY':
-                self.__rangecheckdict__['O2Sat'] = 100*self.__rangecheckdict__['DOXY']/unit.oxy_sol(self.__rangecheckdict__['PSAL'], self.__rangecheckdict__['TEMP'], unit='micromole/kg')
+                # recalculate O2sat if its DOXY
+                if k == 'DOXY':
+                    optode_flag = get_optode_type(int(self.__rangecheckdict__['WMO'])) == 'AANDERAA_OPTODE_4330'
+                    self.__rangecheckdict__['O2Sat'] = 100*self.__rangecheckdict__['DOXY']/unit.oxy_sol(self.__rangecheckdict__['PSAL'], self.__rangecheckdict__['TEMP'], self.__rangecheckdict__['PDEN'], a4330=optode_flag)
 
         self.assign(self.__rangecheckdict__)
+        self.to_dataframe()
     
     def to_dict(self):
         '''
@@ -285,30 +326,47 @@ def to_dataframe(self):
         df = pd.DataFrame()
         df['CYCLE']     = self.CYCLE_GRID
         df['SDN']       = self.SDN_GRID
+        df['WMO']       = self.WMO
         df['LATITUDE']  = self.LATITUDE_GRID
         df['LONGITUDE'] = self.LONGITUDE_GRID
         df['PRES']      = self.PRES
         df['TEMP']      = self.TEMP
+        df['TEMP_QC']   = self.TEMP_QC
         df['PSAL']      = self.PSAL
+        df['PSAL_QC']   = self.PSAL_QC
         df['PDEN']      = self.PDEN
         if 'DOXY' in self.__floatdict__.keys():
             df['DOXY']      = self.DOXY
+            df['DOXY_QC']   = self.DOXY_QC
         if 'CHLA' in self.__floatdict__.keys():
             df['CHLA']      = self.CHLA
+            df['CHLA_QC']   = self.CHLA_QC
         if 'BBP700' in self.__floatdict__.keys():
             df['BBP700']    = self.BBP700
+            df['BBP700_QC'] = self.BBP700_QC
         if 'CDOM' in self.__floatdict__.keys():
             df['CDOM']      = self.CDOM
+            df['CDOM_QC']   = self.CDOM_QC
         if 'DOXY_ADJUSTED' in self.__floatdict__.keys():
             df['DOXY_ADJUSTED']      = self.DOXY_ADJUSTED
+            df['DOXY_ADJUSTED_QC']   = self.DOXY_ADJUSTED_QC
         if 'CHLA_ADJUSTED' in self.__floatdict__.keys():
             df['CHLA_ADJUSTED']      = self.CHLA_ADJUSTED
+            df['CHLA_ADJUSTED_QC']   = self.CHLA_ADJUSTED_QC
         if 'BBP700_ADJUSTED' in self.__floatdict__.keys():
             df['BBP700_ADJUSTED']    = self.BBP700_ADJUSTED
+            df['BBP700_ADJUSTED_QC'] = self.BBP700_ADJUSTED_QC
         if 'CDOM_ADJUSTED' in self.__floatdict__.keys():
             df['CDOM_ADJUSTED']      = self.CDOM_ADJUSTED
+            df['CDOM_ADJUSTED_QC']   = self.CDOM_ADJUSTED_QC
         if 'O2Sat' in self.__floatdict__.keys():
             df['O2Sat']      = self.O2Sat
+            df['O2Sat_QC']   = self.O2Sat_QC
+
+        exvals = [k for k,v in self.__floatdict__.items() if type(v) is not np.ndarray]
+        for key in list(set(self.__floatdict__.keys()) - set(df.columns) - set(exvals)):
+            if self.__floatdict__[key].shape[0] == df.shape[0]:
+                df[key] = self.__floatdict__[key]
 
         self.df = df
 
@@ -327,7 +385,7 @@ def get_track(self):
 
         return copy.deepcopy(self.track)
 
-    def get_ncep(self):
+    def get_ncep(self, verbose=True):
         '''
         Loads NCEP data along the float track
         '''
@@ -339,7 +397,7 @@ def get_ncep(self):
         
         return copy.deepcopy(self.NCEP)
 
-    def get_woa(self):
+    def get_woa(self, verbose=True):
         '''
         Loads WOA data along the float track
         '''
@@ -347,11 +405,11 @@ def get_woa(self):
         if not hasattr(self, 'track'):
             self.get_track()
         
-        self.z_WOA, self.WOA, self.__WOAweights__ = woa_to_float_track(self.track, 'O2sat', local_path=self.woa_path)
+        self.z_WOA, self.WOA, self.__WOAweights__ = woa_to_float_track(self.track, 'O2sat', local_path=self.woa_path, verbose=verbose)
 
         return copy.deepcopy(self.WOA)
 
-    def calc_gains(self, ref='NCEP', zlim=25.):
+    def calc_gains(self, ref='NCEP', zlim=25., verbose=True):
         '''
         Calculate gain values using NCEP or WOA reference data. Uses function
         calc_gain(). 
@@ -363,22 +421,22 @@ def calc_gains(self, ref='NCEP', zlim=25.):
         if ref == 'NCEP':
             # check if reference data is already calculated
             if not hasattr(self, 'NCEP'):
-                self.get_ncep()
+                self.get_ncep(verbose=verbose)
 
             pH2O = unit.pH2O(util.get_var_by('TEMP_DOXY', 'TRAJ_CYCLE', self.__floatdict__))
 
             common_cycles, c1, c2 = np.intersect1d(self.CYCLE, np.unique(self.__floatdict__['TRAJ_CYCLE']), assume_unique=True, return_indices=True)
 
             self.NCEP_PPOX = unit.atmos_pO2(self.NCEP[c1], pH2O[c2])/100
-            self.__NCEPgains__, self.__NCEPfloatref__ = calc_gain(self.__floatdict__, self.NCEP_PPOX)
+            self.__NCEPgains__, self.__NCEPfloatref__ = calc_gain(self.__floatdict__, self.NCEP_PPOX, verbose=verbose)
             self.gains = self.__NCEPgains__
 
         if ref == 'WOA':
             # check if reference data is already calculated
             if not hasattr(self, 'WOA'):
-                self.get_woa()
+                self.get_woa(verbose=verbose)
 
-            self.__WOAgains__, self.__WOAfloatref__, self.__WOAref__ = calc_gain(self.__floatdict__, dict(z=self.z_WOA, WOA=self.WOA), inair=False, zlim=zlim)
+            self.__WOAgains__, self.__WOAfloatref__, self.__WOAref__ = calc_gain(self.__floatdict__, dict(z=self.z_WOA, WOA=self.WOA), inair=False, zlim=zlim, verbose=verbose)
             self.gains = self.__WOAgains__
         
         return copy.deepcopy(self.gains)
@@ -398,6 +456,8 @@ def plot(self, kind, **kwargs):
                 g = fplt.gainplot(self.SDN, self.__NCEPfloatref__[:,2], self.NCEP_PPOX, self.__NCEPgains__, ref)
             elif ref == 'WOA':
                 g = fplt.gainplot(self.SDN, self.__WOAfloatref__[:,2], self.__WOAref__, self.__WOAgains__, ref)
+            else:
+                raise ValueError('Invalid input for keyword argument "ref"')
 
         elif kind == 'cscatter':
             var = kwargs.pop('varname')
@@ -415,6 +475,14 @@ def plot(self, kind, **kwargs):
 
             g = fplt.profiles(self.df, varlist=varlist, **kwargs)
 
+        elif kind == 'qcprofiles':
+            varlist = kwargs.pop('varlist')
+
+            if not hasattr(self, 'df'):
+                self.to_dataframe()
+
+            g = fplt.qc_profiles(self.df, varlist=varlist, **kwargs)
+
         else:
             raise ValueError('Invalid input for keyword argument "kind"')
 
@@ -528,6 +596,7 @@ def compare_independent_data(self, fmt='*', **kwargs):
         meta_keys = set(meta_keys)
 
         meta_data_string = ''
+        cyc = 1
         for label in meta_dict.keys():
             if label == ' ':
                 label = 'Observation'
@@ -550,18 +619,12 @@ def compare_independent_data(self, fmt='*', **kwargs):
         fig = plt.figure()
         ax_list = [fig.add_subplot(1, nvar+map_num, n+1) for n in range(nvar)]
         axes_dict = {v:ax for v, ax in zip(var_keys, ax_list)}
-        print(var_keys)
-        print(ax_list)
         if map_num > 0:
             ax_list.append(fig.add_subplot(1, nvar+map_num, nvar+1, projection=ccrs.PlateCarree()))
         
         ccount = 0
-        if sns_flag:
-            fcol = sns.color_palette('colorblind')[0]
-            clist = sns.color_palette('colorblind')[1:]
-        else:
-            fcol = 'blue'
-            clist = ['orange', 'green', 'cyan', 'red']
+        fcol = sns.color_palette('colorblind')[0]
+        clist = sns.color_palette('colorblind')[1:]
         
         for label in plot_dict.keys():
             pres = plot_dict[label].pop('PRES')
@@ -602,7 +665,6 @@ def compare_independent_data(self, fmt='*', **kwargs):
             mx.plot(np.nan, np.nan, 'o', color=fcol, label='Float {}'.format(self.WMO))
 
             c = np.array(c)
-            print(c)
             minlon, maxlon = np.nanmin(c[:,1]), np.nanmax(c[:,1])
             minlat, maxlat = np.nanmin(c[:,0]), np.nanmax(c[:,0])
 
@@ -621,7 +683,6 @@ def compare_independent_data(self, fmt='*', **kwargs):
             extent[2] = extent[2] - 6
             extent[3] = extent[3] + 6
 
-            print(extent)
             mx.set_extent(extent, crs=ccrs.PlateCarree())
             mx.legend(loc=4, bbox_to_anchor=(1.05, 1.0), fontsize=8)
             mx.add_feature(cfeature.COASTLINE)
@@ -647,24 +708,24 @@ def compare_independent_data(self, fmt='*', **kwargs):
 
         ax_list[0].legend(loc=2, fontsize=10)
 
-        print(meta_data_string)
-
         anc = AnchoredText(meta_data_string,
                 loc=4, frameon=True, prop=dict(size=8))
         ax_list[0].add_artist(anc)
 
+        fig.set_size_inches(10,6)
+
         return fig, ax_list
 
 class profiles:
 
     set_dirs = set_dirs
 
-    def __init__(self, floats, cycles=None, mission='B', mode='RD', keep_fillvalue=False, rcheck=True):
+    def __init__(self, floats, cycles=None, mission='B', mode='RD', keep_fillvalue=False, rcheck=True, verbose=False):
         if type(floats) is int:
             floats = [floats]
 
         self.__argofiles__ = organize_files(get_files(ARGO_PATH, floats, cycles=cycles, mission=mission, mode=mode))
-        self.__floatdict__ = load_profiles(self.__argofiles__)
+        self.__floatdict__ = load_profiles(self.__argofiles__, verbose=verbose)
         self.__rawfloatdict__ = self.__floatdict__
 
         # local path info
@@ -677,12 +738,11 @@ def __init__(self, floats, cycles=None, mission='B', mode='RD', keep_fillvalue=F
             self.rm_fillvalue()
 
         if rcheck:
-            self.check_range('DOXY')
+            self.check_range('all', verbose=verbose)
 
     def assign(self, floatdict):
 
         # metadata and dimension variables
-        self.floatName  = floatdict['floatName']
         self.floatType  = floatdict['floatType']
         self.N_LEVELS   = floatdict['N_LEVELS']
         self.CYCLE      = floatdict['CYCLES']
@@ -707,10 +767,7 @@ def assign(self, floatdict):
             self.PSAL    = floatdict['PSAL']
             self.PSAL_QC = floatdict['PSAL_QC']
             # potential density
-            if flagSA:
-                self.PDEN = gsw.pot_rho_t_exact(gsw.SA_from_SP(self.PSAL, self.PRES, self.LONGITUDE_GRID, self.LATITUDE_GRID), self.TEMP, self.LONGITUDE_GRID, self.LATITUDE_GRID) - 1000
-            else:
-                self.PDEN = pden(self.PSAL, self.TEMP, self.PRES, 0) - 1000
+            self.PDEN = gsw.pot_rho_t_exact(gsw.SA_from_SP(self.PSAL, self.PRES, self.LONGITUDE_GRID, self.LATITUDE_GRID), self.TEMP, self.LONGITUDE_GRID, self.LATITUDE_GRID) - 1000
 
         # bgc variables - not necessarily all there so check if the fields exist
         if 'DOXY' in floatdict.keys():
@@ -748,17 +805,20 @@ def rm_fillvalue(self):
         self.__nofillvaluefloatdict__ = dict_fillvalue_clean(self.__rawfloatdict__)
         self.__floatdict__ = self.__nofillvaluefloatdict__
         self.assign(self.__nofillvaluefloatdict__)
+        self.to_dataframe()
 
     def clean(self, bad_flags=None):
         self.__cleanfloatdict__ = dict_clean(self.__floatdict__, bad_flags=bad_flags)
         self.__floatdict__ = self.__cleanfloatdict__
         self.assign(self.__cleanfloatdict__)
+        self.to_dataframe()
 
     def reset(self):
         self.__floatdict__ = self.__rawfloatdict__
         self.assign(self.__rawfloatdict__)
+        self.to_dataframe()
 
-    def check_range(self, key):
+    def check_range(self, key, verbose=False):
         '''
         Performs a range check for variables that have a RTQC range available.
         Replaces values outside the range with NaN values. Takes string input
@@ -773,12 +833,14 @@ def check_range(self, key):
             key = [key]
         
         for k in key:
-            self.__rangecheckdict__ = range_check(k, self.__floatdict__)
-            self.__floatdict__ = self.__rangecheckdict__
+            if k in self.__floatdict__.keys():
+                self.__rangecheckdict__ = range_check(k, self.__floatdict__, verbose=verbose)
+                self.__floatdict__ = self.__rangecheckdict__
 
-            # recalculate O2sat if its DOXY
-            if k == 'DOXY':
-                self.__rangecheckdict__['O2Sat'] = 100*self.__rangecheckdict__['DOXY']/unit.oxy_sol(self.__rangecheckdict__['PSAL'], self.__rangecheckdict__['TEMP'], unit='micromole/kg')
+                # recalculate O2sat if its DOXY
+                if k == 'DOXY':
+                    optode_flag = get_optode_type(int(self.__rangecheckdict__['WMO'])) == 'AANDERAA_OPTODE_4330'
+                    self.__rangecheckdict__['O2Sat'] = 100*self.__rangecheckdict__['DOXY']/unit.oxy_sol(self.__rangecheckdict__['PSAL'], self.__rangecheckdict__['TEMP'], a4330=optode_flag)
 
         self.assign(self.__rangecheckdict__)
              
@@ -921,10 +983,17 @@ def get_files(local_path, wmo_numbers, cycles=None, mission='B', mode='RD', verb
     local_path = Path(local_path)
 
     if mission == 'B':
+        if '__bgcindex__' not in globals():
+            global __bgcindex__
+            __bgcindex__ = get_index()
         subset_index = __bgcindex__[__bgcindex__.wmo.isin(wmo_numbers)]
-    if mission == 'C':
-        __coreindex__ = io.read_index(mission='C')
-        subset_index = __coreindex__[__coreindex__.wmo.isin(wmo_numbers)]
+    elif mission == 'C':
+        if '__globalindex__' not in globals():
+            global __globalindex__
+            __globalindex__ = get_index(index='global')
+        subset_index = __globalindex__[__globalindex__.wmo.isin(wmo_numbers)]
+    else:
+        raise ValueError('Invalid input for parameter "mission"')
     if cycles is not None:
         subset_index = subset_index[subset_index.cycle.isin(cycles)]
     wcs = ['*' + a + b + '*.nc' for a in mission for b in mode]
@@ -932,7 +1001,7 @@ def get_files(local_path, wmo_numbers, cycles=None, mission='B', mode='RD', verb
 
     matches = [fn for sub in [fnmatch.filter(subset_index.file, w) for w in wcs] for fn in sub]
     subset_index = subset_index[subset_index.file.isin(matches)]
-    local_files = [(local_path / dac / str(wmo) / 'profiles' / fn.split('/')[-1]).as_posix() for dac, wmo, fn in zip(subset_index.dac, subset_index.wmo, subset_index.file)]
+    local_files = [(local_path / dac / str(wmo) / 'profiles' / fn.split('/')[-1]) for dac, wmo, fn in zip(subset_index.dac, subset_index.wmo, subset_index.file)]
 
     remove_ix = []
     for i,fn in enumerate(local_files):
@@ -951,61 +1020,23 @@ def organize_files(files):
     '''
     Sort files according to time they were recorded.
     '''
-    lead_letter = files[0].split('/')[-1][0]
+    lead_letter = files[0].name[0]
     if lead_letter == 'R' or lead_letter == 'D':
         index = get_index('global')
     else:
+        if '__bgcindex__' not in globals():
+            global __bgcindex__
+            __bgcindex__ = get_index()
         index = __bgcindex__
     
-    dates = np.array([index[index.file.str.find(fn.split('/')[-1]) != -1].date.iloc[0] for fn in files])
+    dates = np.array([index[index.file.str.find(fn.name) != -1].date.iloc[0] for fn in files])
     sorted_files = list(np.array(files)[np.argsort(dates)])
 
     return sorted_files
 
-def get_vars(files):
-
-    nc = Dataset(Path(files[0]), 'r')
-    varnames = set(nc.variables.keys())
-    nc.close()
-
-    for fn in files:
-        nc = Dataset(Path(fn), 'r')
-        varnames = varnames.intersection(nc.variables.keys())
-        nc.close()
-
-    varnames = list(varnames)
+def load_traj(local_path, wmo):
 
-    return varnames
-
-def read_qc(flags):
-
-    decode_flags = np.array([f.decode('utf-8') for f in flags])
-    decode_flags[decode_flags == ' '] = '4'
-
-    out_flags = np.array([int(f) for f in decode_flags])
-
-    return out_flags
-
-def get_worst_flag(*args):
-    out_flags = np.ones(args[0].shape)
-
-    if len(args) == 1:
-        out_flags = args[0]
-    else:
-        # make an array where all data marked as good
-        out_flags = np.ones(args[0].shape)
-        # loop through input flags
-        for flags in args:
-            # loop through each datapoint flag
-            for i,f in enumerate(flags):
-                if f > out_flags[i] and f <= 4:
-                    out_flags[i] = f
-                elif f in [5,8] and out_flags[i] <= 2:
-                    out_flags[i] = f
-                elif f == 9:
-                    out_flags[i] = 9
-        
-    return out_flags
+    return trajData, trajFile
 
 def load_argo(local_path, wmo, grid=False, verbose=True):
     '''
@@ -1021,8 +1052,7 @@ def load_argo(local_path, wmo, grid=False, verbose=True):
             - floatName: WMO number, from input
             - floatType: Kind of float (APEX, ARVOR, etc.)
             - N_LEVELS: Number of depth levels, Argo dimension N_LEVELS
-            - N_CYCLES: Number of profiles, Argo dimension N_PROF
-            - CYCLES: Array from 1 to N_CYCLES
+            - N_PROF: Number of profiles, Argo dimension N_PROF
             - LATITUDE: Latitude (-90, 90) for each profile
             - LONGITUDE: Longitude (-180, 180) for each profile
             - SDN: Serial Date Number for each profile
@@ -1102,49 +1132,15 @@ def load_argo(local_path, wmo, grid=False, verbose=True):
     # number of profile cycles
     M = Sprof_nc.dimensions['N_LEVELS'].size
     N = Sprof_nc.dimensions['N_PROF'].size
-    # beginning of output dict with basic info, following variables in SAGEO2
-    floatData = dict(floatName=wmo, N_CYCLES=N, N_LEVELS=M, WMO=int(wmo))
     
-    mask = Sprof_nc.variables['PRES'][:].mask
-    mask_vars = ['TEMP','PSAL']
-    if 'DOXY' in Sprof_nc.variables.keys():
-        mask_vars = mask_vars + ['DOXY']
-    if 'DOXY_ADJUSTED' in Sprof_nc.variables.keys():
-        mask_vars = mask_vars + ['DOXY_ADJUSTED']
-
-    for v in mask_vars:
-        mask = np.logical_or(mask, Sprof_nc.variables[v][:].mask)
-
-    if not 'CYCLE_NUMBER' in Sprof_nc.variables.keys():
-        floatData['CYCLES'] = np.arange(1,N+1)
-    else:
-        floatData['CYCLES'] = Sprof_nc.variables['CYCLE_NUMBER'][:].data.flatten()
-
-    # load in variables that will be in every file
-    floatData['PRES'] = Sprof_nc.variables['PRES'][:].data.flatten()
-    floatData['TEMP'] = Sprof_nc.variables['TEMP'][:].data.flatten()
-    floatData['PSAL'] = Sprof_nc.variables['PSAL'][:].data.flatten()
-    floatData['SDN']  = Sprof_nc.variables['JULD'][:].data.flatten() + mdates.datestr2num('1950-01-01')
-    floatData['LATITUDE']  = Sprof_nc.variables['LATITUDE'][:].data.flatten()
-    floatData['LONGITUDE'] = Sprof_nc.variables['LONGITUDE'][:].data.flatten()
-
-    # loop through other possible BGC variables
-    bgc_vars = ['DOXY', 'CHLA', 'BBP700', 'CDOM', 'NITRATE', 'DOWNWELLING_IRRADIANCE']
-    core_vars = ['PRES', 'TEMP', 'PSAL', 'POSITION']
-    for v in bgc_vars:
-        if v in Sprof_nc.variables.keys():
-            floatData[v] = Sprof_nc.variables[v][:].data.flatten()
-
-    for v in bgc_vars + core_vars:
-        v_qc = v + '_QC'
-        if v_qc in Sprof_nc.variables.keys():
-            floatData[v_qc] = read_qc(Sprof_nc.variables[v_qc][:].data.flatten())
-        v_adj = v + '_ADJUSTED'
-        if v_adj in Sprof_nc.variables.keys():
-            floatData[v_adj] = Sprof_nc.variables[v_adj][:].data.flatten()
-            v_adj_qc = v_adj + '_QC'
-            if v_adj_qc in Sprof_nc.variables.keys():
-                floatData[v_adj_qc] = read_qc(Sprof_nc.variables[v_adj_qc][:].data.flatten())
+    floatData = read_all_variables(Sprof_nc)
+    floatData['SDN']  = floatData['JULD'] + mdates.datestr2num('1950-01-01')
+    floatData['CYCLES'] = floatData['CYCLE_NUMBER']
+    floatData['WMO'] = wmo
+
+    qc_keys = [s for s in floatData.keys() if '_QC' in s and 'PROFILE' not in s]
+    for qc in qc_keys:
+        floatData[qc] = util.read_qc(floatData[qc])
 
     if grid:
         ftype = ''
@@ -1157,112 +1153,57 @@ def load_argo(local_path, wmo, grid=False, verbose=True):
         floatData['CYCLE_GRID']     = np.tile(floatData['CYCLES'],(M,1)).T.flatten()
         floatData['LATITUDE_GRID']  = np.tile(floatData['LATITUDE'],(M,1)).T.flatten()
         floatData['LONGITUDE_GRID'] = np.tile(floatData['LONGITUDE'],(M,1)).T.flatten()
+        floatData['PDEN'] = gsw.pot_rho_t_exact(gsw.SA_from_SP(floatData['PSAL'], floatData['PRES'], floatData['LONGITUDE_GRID'], floatData['LATITUDE_GRID']), floatData['TEMP'], floatData['PRES'], 0)
 
-    floatData['O2Sat'] = 100*floatData['DOXY']/unit.oxy_sol(floatData['PSAL'], floatData['TEMP'], unit='micromole/kg')
-    # match the fill values
-    ix = np.logical_or(np.logical_or(floatData['PSAL'] >= 99999., floatData['TEMP'] >= 99999.), floatData['DOXY'] >= 99999.)
-    floatData['O2Sat'][ix] = 99999.
-    # get the worst QC flag from each quantity that goes into the calculation
-    floatData['O2Sat_QC'] = get_worst_flag(floatData['TEMP_QC'], floatData['PSAL_QC'], floatData['DOXY_QC'])
+    if 'DOXY' in floatData.keys():
+        optode_flag = get_optode_type(int(wmo)) == 'AANDERAA_OPTODE_4330'
+        floatData['O2Sat'] = 100*floatData['DOXY']/unit.oxy_sol(floatData['PSAL'], floatData['TEMP'], floatData['PDEN'], a4330=optode_flag)
+        # match the fill values
+        ix = np.logical_or(np.logical_or(floatData['PSAL'] >= 99999., floatData['TEMP'] >= 99999.), floatData['DOXY'] >= 99999.)
+        floatData['O2Sat'][ix] = 99999.
+        # get the worst QC flag from each quantity that goes into the calculation
+        floatData['O2Sat_QC'] = util.get_worst_flag(floatData['TEMP_QC'], floatData['PSAL_QC'], floatData['DOXY_QC'])
 
     if BRtraj_flag:
-        if 'PPOX_DOXY' in BRtraj_nc.variables.keys():
+        if 'PPOX_DOXY' in BRtraj_nc.variables.keys() and 'TEMP_DOXY' in BRtraj_nc.variables.keys():
             floatData['PPOX_DOXY']  = BRtraj_nc.variables['PPOX_DOXY'][:].data.flatten()
-        elif 'DOXY' in BRtraj_nc.variables.keys():
-            #  unit conversion from umol kg-1 to pO2, some shaky S and P assumptions?
+            floatData['TEMP_DOXY']  = BRtraj_nc.variables['TEMP_DOXY'][:].data.flatten()
+            floatData['TRAJ_CYCLE'] = BRtraj_nc.variables['CYCLE_NUMBER'][:].data.flatten()
+            floatData['inair']      = True
+        elif 'DOXY' in BRtraj_nc.variables.keys() and 'TEMP_DOXY' in BRtraj_nc.variables.keys():
+            # unit conversion from umol kg-1 to pO2, some shaky S and P assumptions?
             floatData['PPOX_DOXY'] = unit.doxy_to_pO2(unit.umol_per_sw_to_mmol_per_L(
                 BRtraj_nc.variables['DOXY'][:].data.flatten(),
                 0, # salinity is 0 in air???
                 BRtraj_nc.variables['TEMP_DOXY'][:].data.flatten(),
                 0 # pressure is 0 in air???
             ), 0, BRtraj_nc.variables['TEMP_DOXY'][:].data.flatten())
-        floatData['TEMP_DOXY']  = BRtraj_nc.variables['TEMP_DOXY'][:].data.flatten()
-        floatData['TRAJ_CYCLE'] = BRtraj_nc.variables['CYCLE_NUMBER'][:].data.flatten()
-        floatData['inair']      = True
+            floatData['TEMP_DOXY']  = BRtraj_nc.variables['TEMP_DOXY'][:].data.flatten()
+            floatData['TRAJ_CYCLE'] = BRtraj_nc.variables['CYCLE_NUMBER'][:].data.flatten()
+            floatData['inair']      = True
+        else:
+            floatData['inair']      = False
     else:
-        floatData['inair']      = False
+        floatData['inair']          = False
 
     return floatData, Sprof, BRtraj, meta
 
-# def load_profile(fn):
-#     '''
-#     Function to load a singe Argo profile file into a dict() object
-#     NOTE: Deprecated, use load_profiles instead, which can handle multiple
-#     profile files at once, but produces the same result for just one. 
+def load_profiles(files, verbose=False):
+
+    common_variables = util.get_vars(files)
+    core_files = len(files)*[' ']
+    for i,f in enumerate(files):
+        data_mode = f.name[1]
+        if data_mode == 'D':
+            core_files[i] = f.parent / f.name.replace('B','')
+        else:
+            test_file = f.parent / f.name.replace('B','')
+            if not test_file.exists():
+                test_file = f.parent / f.name.replace('BR', 'D')
+                if not test_file.exists():
+                    raise FileNotFoundError('Corresponding core file not found')
+            core_files[i] = test_file
 
-#     Author:   
-#         Christopher Gordon
-        
-#         Fisheries and Oceans Canada
-        
-#         chris.gordon@dfo-mpo.gc.ca
-    
-#     Last update: 29-04-2020
-    
-#     Change log:
-    
-#         - 22-04-2020: updated so that pressure mask determines all variables - need to add all quality flags to output
-    
-#         - 29-04-2020: switched file/path handling from os module to pathlib
-
-#         - 24-06-2020: deprecated, re-wrote as load_profiles()
-#     '''
-
-    # # try to load the profile as absolute path or relative path
-    # try:
-    #     nc = Dataset(fn, 'r')
-    # except:
-    #     try:
-    #         nc = Dataset(Path(ARGO_PATH) / fn, 'r')
-    #     except:
-    #         raise FileNotFoundError('No such file {} or {}'.format(fn, str(Path(ARGO_PATH) / fn)))
-
-    # wmo = ''
-    # for let in nc.variables['PLATFORM_NUMBER'][:].compressed():
-    #     wmo = wmo + let.decode('UTF-8')
-
-    # cycle = nc.variables['CYCLE_NUMBER'][:].compressed()[0]
-
-    # # number of profile cycles
-    # M = nc.dimensions['N_LEVELS'].size
-    # # beginning of output dict with basic info, following variables in SAGEO2
-    # floatData = dict(floatName=wmo, N_LEVELS=M, CYCLE=cycle)
-
-    # ftype = ''
-    # for let in nc.variables['PLATFORM_TYPE'][:].compressed():
-    #     ftype = ftype + let.decode('UTF-8')
-
-    # floatData['floatType'] = ftype
-
-    # # load in variables that will be in every file
-    # floatData['PRES'] = nc.variables['PRES'][:].data
-    # floatData['TEMP'] = nc.variables['TEMP'][:].data
-    # floatData['PSAL'] = nc.variables['PSAL'][:].data
-    # floatData['SDN']  = nc.variables['JULD'][:].data + mdates.datestr2num('1950-01-01')
-    # floatData['LATITUDE']  = nc.variables['LATITUDE'][:].data
-    # floatData['LONGITUDE'] = nc.variables['LONGITUDE'][:].data
-
-    # # loop through other possible BGC variables
-    # bgc_vars = ['DOXY', 'CHLA', 'BBP700', 'CDOM', 'NITRATE', 'DOWNWELLING_IRRADIANCE']
-    # for v in bgc_vars:
-    #     if v in nc.variables.keys():
-    #         floatData[v] = nc.variables[v][:].data
-
-    # for v in floatData.keys():
-    #     v_qc = v + '_QC'
-    #     if v_qc in nc.variables.keys():
-    #         floatData[v_qc] = nc.variables[v_qc][:].data
-    #     v_adj = v + '_ADJUSTED'
-    #     if v_adj in nc.variables.keys():
-    #         floatData[v_adj] = nc.variables[v_adj][:].data
-    #         floatData[v_adj + '_QC'] = nc.variabes[v_adj + '_QC'][:].data
-
-    # return floatData
-
-def load_profiles(files):
-
-    common_variables = get_vars(files)
-    core_files = [fn.replace('B','') for fn in files]
 
     floatData = dict(
         floatName=[], N_LEVELS=[], N_PROF=[], CYCLES=np.array([], dtype=int), floatType=[]
@@ -1275,16 +1216,16 @@ def load_profiles(files):
     for v in ['WMO', 'LATITUDE', 'LONGITUDE', 'POSITION_QC', 'SDN_GRID', 'LATITUDE_GRID', 'LONGITUDE_GRID', 'CYCLE_GRID']:
         floatData[v] = np.array([])
 
-    for v in ['DOXY', 'CHLA', 'BBP700', 'CDOM', 'NITRATE', 'DOWNWELLING_IRRADIANCE']:
-        if v in common_variables:
-            floatData[v] = np.array([])
-            floatData[v + '_QC'] = np.array([])
-            if v + '_ADJUSTED' in common_variables:
-                floatData[v + '_ADJUSTED'] = np.array([])
-                floatData[v + '_ADJUSTED' + '_QC'] = np.array([])
+    for v in common_variables:
+        floatData[v] = np.array([])
+        floatData[v + '_QC'] = np.array([])
+        if v + '_ADJUSTED' in common_variables:
+            floatData[v + '_ADJUSTED'] = np.array([])
+            floatData[v + '_ADJUSTED' + '_QC'] = np.array([])
 
-    for fn,cn in zip(files,core_files):
-        print(fn, cn)
+    for fn, cn in zip(files, core_files):
+        if verbose:
+            print(fn, cn)
         # try to load the profile as absolute path or relative path
         try:
             nc = Dataset(fn, 'r')
@@ -1330,40 +1271,79 @@ def load_profiles(files):
         floatData['WMO']        = np.append(floatData['WMO'], np.array(M*N*[wmo]))
 
         # load in variables that will be in every file
-        floatData['PRES'] = np.append(floatData['PRES'], cc.variables['PRES'][:].data.flatten())
-        floatData['PRES_QC'] = np.append(floatData['PRES_QC'], read_qc(cc.variables['PRES_QC'][:].data.flatten()))
-        floatData['TEMP'] = np.append(floatData['TEMP'], cc.variables['TEMP'][:].data.flatten())
-        floatData['TEMP_QC'] = np.append(floatData['TEMP_QC'], read_qc(cc.variables['TEMP_QC'][:].data.flatten()))
-        floatData['PSAL'] = np.append(floatData['PSAL'], cc.variables['PSAL'][:].data.flatten())
-        floatData['PSAL_QC'] = np.append(floatData['PSAL_QC'], read_qc(cc.variables['PSAL_QC'][:].data.flatten()))
-        floatData['SDN'] = np.append(floatData['SDN'], cc.variables['JULD'][:].data.flatten() + mdates.datestr2num('1950-01-01'))
-        floatData['SDN_QC'] = np.append(floatData['SDN_QC'], read_qc(cc.variables['JULD_QC'][:].data.flatten()))
-        floatData['SDN_GRID'] = np.append(floatData['SDN_GRID'], np.array(N*M*[np.nanmean(cc.variables['JULD'][:].data.flatten() + mdates.datestr2num('1950-01-01'))]))
-        floatData['LATITUDE'] = np.append(floatData['LATITUDE'], cc.variables['LATITUDE'][:].data.flatten())
-        floatData['LATITUDE_GRID'] = np.append(floatData['LATITUDE_GRID'], np.array(N*M*[np.nanmean(cc.variables['LATITUDE'][:].data.flatten())]))
-        floatData['LONGITUDE'] = np.append(floatData['LONGITUDE'], cc.variables['LONGITUDE'][:].data.flatten())
+        floatData['PRES']           = np.append(floatData['PRES'], cc.variables['PRES'][:].data.flatten())
+        floatData['PRES_QC']        = np.append(floatData['PRES_QC'], util.read_qc(cc.variables['PRES_QC'][:].data.flatten()))
+        floatData['TEMP']           = np.append(floatData['TEMP'], cc.variables['TEMP'][:].data.flatten())
+        floatData['TEMP_QC']        = np.append(floatData['TEMP_QC'], util.read_qc(cc.variables['TEMP_QC'][:].data.flatten()))
+        floatData['PSAL']           = np.append(floatData['PSAL'], cc.variables['PSAL'][:].data.flatten())
+        floatData['PSAL_QC']        = np.append(floatData['PSAL_QC'], util.read_qc(cc.variables['PSAL_QC'][:].data.flatten()))
+        floatData['SDN']            = np.append(floatData['SDN'], cc.variables['JULD'][:].data.flatten() + mdates.datestr2num('1950-01-01'))
+        floatData['SDN_QC']         = np.append(floatData['SDN_QC'], util.read_qc(cc.variables['JULD_QC'][:].data.flatten()))
+        floatData['SDN_GRID']       = np.append(floatData['SDN_GRID'], np.array(N*M*[np.nanmean(cc.variables['JULD'][:].data.flatten() + mdates.datestr2num('1950-01-01'))]))
+        floatData['LATITUDE']       = np.append(floatData['LATITUDE'], cc.variables['LATITUDE'][:].data.flatten())
+        floatData['LATITUDE_GRID']  = np.append(floatData['LATITUDE_GRID'], np.array(N*M*[np.nanmean(cc.variables['LATITUDE'][:].data.flatten())]))
+        floatData['LONGITUDE']      = np.append(floatData['LONGITUDE'], cc.variables['LONGITUDE'][:].data.flatten())
         floatData['LONGITUDE_GRID'] = np.append(floatData['LONGITUDE_GRID'], np.array(N*M*[np.nanmean(cc.variables['LONGITUDE'][:].data.flatten())]))
-        floatData['POSITION_QC'] = np.append(floatData['POSITION_QC'], read_qc(cc.variables['POSITION_QC'][:].data.flatten()))
+        floatData['POSITION_QC']    = np.append(floatData['POSITION_QC'], util.read_qc(cc.variables['POSITION_QC'][:].data.flatten()))
 
+        print(common_variables)
         # loop through other possible BGC variables
-        bgc_vars = ['DOXY', 'CHLA', 'BBP700', 'CDOM', 'NITRATE', 'DOWNWELLING_IRRADIANCE']
-        for v in bgc_vars:
-            if v in common_variables:
+        for v in common_variables:
+            var_check   = v in nc.variables.keys() and 'N_LEVELS' in nc.variables[v].dimensions
+            dtype_check = nc.variables[v].dtype == 'float32' or nc.variables[v].dtype == 'float64'
+            check = var_check and dtype_check
+            if check:
                 floatData[v] = np.append(floatData[v], vertically_align(cc.variables['PRES'][:].data.flatten(), nc.variables['PRES'][:].data.flatten(), nc.variables[v][:].data.flatten()))
-            v_adj = v + '_ADJUSTED'
-            if v_adj in common_variables:
-                floatData[v_adj] = np.append(floatData[v_adj], vertically_align(cc.variables['PRES'][:].data.flatten(), nc.variables['PRES'][:].data.flatten(), nc.variables[v_adj][:].data.flatten()))
 
         floatData['dPRES'] = delta_pres(cc.variables['PRES'][:].data.flatten(), nc.variables['PRES'][:].data.flatten())
 
         for v in floatData.keys():
             v_qc = v + '_QC'
             if v_qc in common_variables:
-                floatData[v_qc] = np.append(floatData[v_qc], read_qc(nc.variables[v_qc][:].data.flatten()))
+                floatData[v_qc] = np.append(floatData[v_qc], util.read_qc(nc.variables[v_qc][:].data.flatten()))
 
         if 'DOXY' in floatData.keys():
-            floatData['O2Sat'] = 100*floatData['DOXY']/unit.oxy_sol(floatData['PSAL'], floatData['TEMP'], unit='micromole/kg')
-            floatData['O2Sat_QC'] = get_worst_flag(floatData['TEMP_QC'], floatData['PSAL_QC'], floatData['DOXY_QC'])
+            floatData['O2Sat'] = 100*floatData['DOXY']/unit.oxy_sol(floatData['PSAL'], floatData['TEMP'])
+            floatData['O2Sat_QC'] = util.get_worst_flag(floatData['TEMP_QC'], floatData['PSAL_QC'], floatData['DOXY_QC'])
+        
+    return floatData
+
+def read_all_variables(nc):
+    '''
+    Read all variables and dimensions from an Argo netCDF file.
+
+    Args:
+        nc: a netCDF file object
+    
+    Returns:
+        floatData: python dict with all variable and dimension names
+    '''
+
+    floatData = dict()
+    for name, dim in nc.dimensions.items():
+        floatData[name] = dim.size
+    for name, var in nc.variables.items():
+        floatData[name] = var[:].data.flatten()
+
+    return floatData
+
+def read_sprof_gridded_variables(nc):
+    '''
+    Read all variables and dimensions from an Argo Sprof file, do not flatten
+    arrays, keep as 2D arrays.
+
+    Args:
+        nc: a netCDF file object
+    
+    Returns:
+        floatData: python dict with all variable and dimension names
+    '''
+
+    floatData = dict()
+    for name, dim in nc.dimensions.items():
+        floatData[name] = dim.size
+    for name, var in nc.variables.items():
+        floatData[name] = var[:].data
 
     return floatData
 
@@ -1396,7 +1376,7 @@ def read_history_qctest(nc):
 def dict_clean(float_data, bad_flags=None):
 
     clean_float_data = copy.deepcopy(float_data)
-    qc_flags = [k for k in clean_float_data.keys() if '_QC' in k]
+    qc_flags = [k for k in clean_float_data.keys() if '_QC' in k and 'PROFILE' not in k]
 
     if bad_flags is None:
         for qc_key in qc_flags:
@@ -1429,7 +1409,7 @@ def dict_clean(float_data, bad_flags=None):
 def dict_fillvalue_clean(float_data):
 
     clean_float_data = copy.deepcopy(float_data)
-    qc_keys = [k for k in clean_float_data.keys() if '_QC' in k and 'SDN' not in k]
+    qc_keys = [k for k in clean_float_data.keys() if '_QC' in k and 'SDN' not in k and 'PROFILE' not in k]
 
     for k in qc_keys:
         data_key   = k.replace('_QC','')
@@ -1460,7 +1440,7 @@ def track(float_data):
 
     return track
 
-def woa_to_float_track(track, param, zlim=(0,1000), local_path='./'):
+def woa_to_float_track(track, param, zlim=(0,1000), local_path='./', verbose=True):
     '''
     Function to load WOA18 climatological data for comparison with autonomous
     floats. Data to be interpolated along the provided track (t, lat, lon).
@@ -1495,8 +1475,8 @@ def woa_to_float_track(track, param, zlim=(0,1000), local_path='./'):
     Change log:
     '''
 
-    xtrack, woa_track, woa_data = io.load_woa_data(track, param, zlim=zlim, local_path=local_path)
-    woa_interp, wt, yrday = interp.interp_woa_data(xtrack, woa_track, woa_data)
+    xtrack, woa_track, woa_data = io.load_woa_data(track, param, zlim=zlim, local_path=local_path, verbose=verbose)
+    woa_interp, wt, yrday = interp.interp_woa_data(xtrack, woa_track, woa_data, verbose=verbose)
     z = woa_track[0]
 
     return z, woa_interp, wt
@@ -1541,7 +1521,7 @@ def calc_gain(data, ref, inair=True, zlim=25., verbose=True):
     comparison is not available.
     
     Args:
-        data: float data dict object, output from load_argo_data()
+        data: float data dict object, output from load_argo()
         ref: reference data set, either NCEP pO2 or WOA O2sat
         inair: boolean flag to indicate if comparison to NCEP in-air
             data or WOA surface data should be done, default to
@@ -1686,10 +1666,6 @@ def calc_gain_with_carryover(pO2_opt_air, pO2_ref_air, pO2_opt_water):
     return gains, carry_over
 
 
-def grid_var(gridded_cycle, Nprof, Nlevel, argo_var):
-
-    return gV
-
 def vertically_align(P1, P2, V2):
 
 	out = np.nan*np.ones(P1.shape)
@@ -1733,7 +1709,7 @@ def range_check(key, floatdict, verbose=True):
 
     return cleandict
 
-def calc_fixed_doxy_adjusted_error(floatdict, fix_err=10, zlim=25):
+def calc_fixed_doxy_adjusted_error(floatdict, fix_err=10):
     '''
     Calculate DOXY_ADJUSTED_ERROR for fixed partial pressure of 10 mbar 
     PPOX_DOXY.
@@ -1743,7 +1719,7 @@ def calc_fixed_doxy_adjusted_error(floatdict, fix_err=10, zlim=25):
     T = floatdict['TEMP']
     P = floatdict['PRES']
 
-    error = unit.pO2_to_doxy(np.array(S.shape[0]*[10]), S, T, P=P)
+    error = unit.pO2_to_doxy(np.array(S.shape[0]*[fix_err]), S, T, P=P)
 
     return error
 
@@ -1754,6 +1730,11 @@ def oxy_b(dt, tau):
 def oxy_a(dt, tau):
     return 1 - 2*oxy_b(dt, tau)
 
+# hard code the LUT table value so I don't have to 
+# ship the text file with the package
+### is this the right/ok way to do this??? feels wrong ###
+from .lut import lut as lut_data
+
 def correct_response_time(t, DO, T, thickness):
 
     # convert time to seconds
@@ -1767,7 +1748,6 @@ def correct_response_time(t, DO, T, thickness):
 
     # load temperature, boundary layer thickness, and tau matrix from 
     # look-up table provided in the supplement to Bittig and Kortzinger (2017)
-    lut_data = np.loadtxt(REF_PATH / 'T_lL_tau_3830_4330.dat')
     lut_lL = lut_data[0,1:]
     lut_T  = lut_data[1:,0]
     tau100 = lut_data[1:,1:]
@@ -1776,7 +1756,6 @@ def correct_response_time(t, DO, T, thickness):
     # translate boundary layer thickness to temperature dependent tau
     f_thickness = interp2d(lut_T, lut_lL, tau100.T, bounds_error=False)
     tau_T = np.squeeze(f_thickness(mean_temp, thickness))[0,:]
-    print(tau_T)
     # loop through oxygen data 
     for i in range(N-1):
         dt = t_sec[i+1] - t_sec[i]
@@ -1811,3 +1790,21 @@ def correct_response_time_Tconst(t, DO, tau):
     DO_out = f(t_sec)
 
     return DO_out
+
+def get_optode_type(wmo):
+    if '__metaindex__' not in globals():
+        global __metaindex__
+        __metaindex__ = get_index(index='meta')
+    
+    ix = __metaindex__[__metaindex__.wmo == wmo]
+
+    local_file = Path(ARGO_PATH) / ix.dac.iloc[0] / str(wmo) / ix.file.iloc[0].split('/')[-1]
+    nc = Dataset(local_file)
+
+    doxy_index = util.get_parameter_index(nc['SENSOR'][:].data, 'OPTODE_DOXY')
+    if doxy_index.shape[0] == 0:
+        return 'NO_OPTODE_FOUND'
+    else:
+        optode_type = util.read_ncstr(nc['SENSOR_MODEL'][:].data[doxy_index[0], :])
+        return optode_type
+
diff --git a/build/lib/bgcArgoDMQC/fplt.py b/build/lib/bgcArgoDMQC/fplt.py
index 4f137ab..44b3434 100644
--- a/build/lib/bgcArgoDMQC/fplt.py
+++ b/build/lib/bgcArgoDMQC/fplt.py
@@ -1,21 +1,15 @@
-import sys
-
 import numpy as np
 
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 
-try:
-    import seaborn as sns
-    sns.set(style='ticks', context='paper', palette='colorblind')
-except:
-    sys.stdout.write('Although the seaborn package is not required, it is recommended for plotting - see https://seaborn.pydata.org/\n')
+import seaborn as sns
+sns.set(style='ticks', context='paper', palette='colorblind')
 
 try:
     import cmocean.cm as cmo
     cmocean_flag = True
 except:
-    sys.stdout.write('Although the cmocean package is not required, it is recommended for plotting - see https://matplotlib.org/cmocean/\n')
     cmocean_flag = False
 
 class pltClass:
@@ -232,7 +226,7 @@ def var_cscatter(df, varname='DOXY', cmap=None, ax=None, ylim=(0,2000), clabel=N
 
     return g
 
-def profiles(df, varlist=['DOXY'], Ncycle=1, Nprof=1, zvar='PRES', xlabels=None, ylabel=None, axes=None, ylim=None, **kwargs):
+def profiles(df, varlist=['DOXY'], Ncycle=1, Nprof=np.inf, zvar='PRES', xlabels=None, ylabel=None, axes=None, ylim=None, **kwargs):
 
     if xlabels is None:
         var_units = dict(
@@ -251,7 +245,9 @@ def profiles(df, varlist=['DOXY'], Ncycle=1, Nprof=1, zvar='PRES', xlabels=None,
             DOXY_ADJUSTED='Diss. Oxygen ($\mathregular{\mu}$mol kg$^{-1}$)',
             DOWNWELLING_IRRADIANCE='Downwelling Irradiance (W m$^{-2}$)',
         )
-        xlabels = [var_units[v] for v in varlist]
+        xlabels = [var_units[v] if v in var_units.keys() else '' for v in varlist]
+
+    cm = plt.cm.gray_r
 
     if axes is None:
         fig, axes = plt.subplots(1, len(varlist), sharey=True)
@@ -276,12 +272,22 @@ def profiles(df, varlist=['DOXY'], Ncycle=1, Nprof=1, zvar='PRES', xlabels=None,
     df.loc[df[zvar] > ylim[1]*1.1] = np.nan
 
     CYCNUM = df.CYCLE.unique()
+    greyflag = False
+    if not 'color' in kwargs.keys():
+        greyflag = True
+    else:
+        c = kwargs.pop('color')
+
+    if Nprof > CYCNUM.shape[0]:
+        Nprof = CYCNUM.shape[0]
 
     for i,v in enumerate(varlist):
         for n in range(Nprof):
             subset_df = df.loc[df.CYCLE == CYCNUM[Ncycle-1 + n-1]]
 
-            axes[i].plot(subset_df[v], subset_df[zvar], **kwargs)
+            if greyflag:
+                c = cm(0.75*(CYCNUM[Ncycle-1 + n-1]/CYCNUM[-1])+0.25)
+            axes[i].plot(subset_df[v], subset_df[zvar], color=c, **kwargs)
             
         axes[i].set_ylim(ylim[::-1])
         axes[i].set_xlabel(xlabels[i])
@@ -303,3 +309,83 @@ def profiles(df, varlist=['DOXY'], Ncycle=1, Nprof=1, zvar='PRES', xlabels=None,
     g.axes = axes
 
     return g
+
+def qc_profiles(df, varlist=['DOXY'], Ncycle=1, Nprof=np.inf, zvar='PRES', xlabels=None, ylabel=None, axes=None, ylim=None, **kwargs):
+
+    if xlabels is None:
+        var_units = dict(
+            TEMP='Temperature ({}C)'.format(chr(176)),
+            TEMP_ADJUSTED='Temperature ({}C)'.format(chr(176)),
+            PSAL='Practical Salinity', 
+            PSAL_ADJUSTED='Practical Salinity', 
+            PDEN='Potential Density (kg m$^{-3}$)',
+            CHLA='Chlorophyll (mg m$^{-3}$',
+            CHLA_ADJUSTED='Chlorophyll (mg m$^{-3}$',
+            BBP700='$\mathsf{b_{bp}}$ (m$^{-1}$)',
+            BBP700_ADJUSTED='$\mathsf{b_{bp}}$ (m$^{-1}$)',
+            CDOM='CDOM (mg m$^{-3}$)',
+            CDOM_ADJUSTED='CDOM (mg m$^{-3}$)',
+            DOXY='Diss. Oxygen ($\mathregular{\mu}$mol kg$^{-1}$)',
+            DOXY_ADJUSTED='Diss. Oxygen ($\mathregular{\mu}$mol kg$^{-1}$)',
+            DOWNWELLING_IRRADIANCE='Downwelling Irradiance (W m$^{-2}$)',
+        )
+        xlabels = [var_units[v] for v in varlist]
+
+    if axes is None:
+        fig, axes = plt.subplots(1, len(varlist), sharey=True)
+        if len(varlist) == 1:
+            axes = [axes]
+    elif len(varlist) > 1:
+        fig = axes[0].get_figure()
+    else:
+        fig = axes.get_figure()
+        axes = [axes]
+
+    if ylim is None:
+        if zvar == 'PRES':
+            ylim=(0,2000)
+            if ylabel is None:
+                ylabel = 'Pressure (dbar)'
+        elif zvar == 'PDEN':
+            ylim = (df.PDEN.min(), df.PDEN.max())
+            if ylabel is None:
+                ylabel = 'Density (kg m$^{-3}$)'
+
+    df.loc[df[zvar] > ylim[1]*1.1] = np.nan
+
+    CYCNUM = df.CYCLE.unique()
+
+    if Nprof > CYCNUM.shape[0]:
+        Nprof = CYCNUM.shape[0]
+
+    groups = {'Good':[1,2,5], 'Probably Bad':[3], 'Bad':[4], 'Interpolated':[8]}
+    colors = {'Good':'green', 'Probably Bad':'yellow', 'Bad':'red', 'Interpolated':'blue'}
+
+    for i,v in enumerate(varlist):
+        vqc = v + '_QC'
+        for n in range(Nprof):
+            subset_df = df.loc[df.CYCLE == CYCNUM[Ncycle-1 + n-1]]
+            for k,f in groups.items():
+                flag_subset_df = subset_df[subset_df[vqc].isin(f)]
+                axes[i].plot(flag_subset_df[v], flag_subset_df[zvar], 'o', markeredgewidth=0.1, markeredgecolor='k', markerfacecolor=colors[k], **kwargs)
+                
+        axes[i].set_ylim(ylim[::-1])
+        axes[i].set_xlabel(xlabels[i])
+
+    subset_df = df.loc[df.CYCLE == CYCNUM[Ncycle-1]]
+    date = mdates.num2date(subset_df.SDN.iloc[0]).strftime('%d %b, %Y')
+
+    axes[0].set_ylabel(ylabel)
+    if Nprof != 1:
+        axes[0].set_title('Cyc. {:d}-{:d}, {}'.format(int(CYCNUM[Ncycle-1]), int(CYCNUM[Ncycle-1+Nprof-1]), date))
+    else:
+        axes[0].set_title('Cyc. {:d}, {}'.format(int(CYCNUM[Ncycle-1]), date))
+
+    w, h = fig.get_figwidth(), fig.get_figheight()
+    fig.set_size_inches(w*len(varlist)/3, h)
+
+    g = pltClass()
+    g.fig  = fig
+    g.axes = axes
+
+    return g
diff --git a/build/lib/bgcArgoDMQC/io.py b/build/lib/bgcArgoDMQC/io.py
index 0486b5c..0d6efa2 100644
--- a/build/lib/bgcArgoDMQC/io.py
+++ b/build/lib/bgcArgoDMQC/io.py
@@ -11,6 +11,7 @@
 
 from netCDF4 import Dataset
 
+from . import configure
 from . import util
 
 global index_path
@@ -18,6 +19,29 @@
 if not index_path.exists():
     index_path.mkdir()
 
+global URL_DICT
+URL_DICT = {
+    'ftp.ifremer.fr':'ftp.ifremer.fr', 
+    'ifremer':'ftp.ifremer.fr', 
+    'coriolis':'ftp.ifremer.fr', 
+    'usgodae.org':'usgodae.org', 
+    'godae':'usgodae.org', 
+    'us':'usgodae.org'
+}
+
+global URL_DIR_DICT
+URL_DIR_DICT = {
+    'ftp.ifremer.fr':'/ifremer/argo/', 
+    'usgodae.org':'/pub/outgoing/argo/', 
+}
+
+config = configure.read_config()
+if 'default_url' in config.keys():
+    url_name = config.pop('default_url')
+    url = URL_DICT[url_name]
+else:
+    url = 'ftp.ifremer.fr'
+
 def index_exists():
 
     meta  = 'ar_index_global_meta.txt.gz'
@@ -31,7 +55,7 @@ def index_exists():
 
     return all([local_meta.exists(), local_index.exists(), local_bgc.exists(), local_synth.exists()])
 
-def read_index(mission='B', remote=False):
+def read_index(mission='B', remote=False, url=url):
     '''
     Function to read and extract information from Argo global index,
     then save it to a dataframe for faster access.
@@ -40,18 +64,22 @@ def read_index(mission='B', remote=False):
         mission (str): *B*, *C*, *S*, or *M* for biogeochemical, global/core, synthetic, and metadata indices respectfully. 
     '''
 
+    url_dir = URL_DIR_DICT[url]
     if mission == 'B':
         local_filename = index_path / 'argo_bio-profile_index.txt.gz'
-        remote_filename = 'ftp://ftp.ifremer.fr/ifremer/argo/argo_bio-profile_index.txt.gz'
+        remote_filename = 'ftp://' + url + (Path(url_dir) / 'argo_bio-profile_index.txt.gz').as_posix()
     elif mission == 'C':
         local_filename = index_path / 'ar_index_global_prof.txt.gz'
-        remote_filename = 'ftp://ftp.ifremer.fr/ifremer/argo/ar_index_global_prof.txt.gz'
+        remote_filename = 'ftp://' + url + (Path(url_dir) / 'ar_index_global_prof.txt.gz').as_posix()
     elif mission == 'S':
         local_filename = index_path / 'argo_synthetic-profile_index.txt.gz'
-        remote_filename = 'ftp://ftp.ifremer.fr/ifremer/argo/argo_synthetic-profile_index.txt.gz'
+        remote_filename = 'ftp://' + url + (Path(url_dir) / 'argo_synthetic-profile_index.txt.gz').as_posix()
     elif mission == 'M':
         local_filename = index_path / 'ar_index_global_meta.txt.gz'
-        remote_filename = 'ftp://ftp.ifremer.fr/ifremer/argo/ar_index_global_meta.txt.gz'
+        remote_filename = 'ftp://' + url + (Path(url_dir) / 'ar_index_global_meta.txt.gz').as_posix()
+    elif mission == 'T':
+        local_filename = index_path / 'ar_index_global_traj.txt.gz'
+        remote_filename = 'ftp://' + url + (Path(url_dir) / 'ar_index_global_traj.txt.gz').as_posix()
     else:
         raise ValueError('Input {} not recognized'.format(mission))
 
@@ -65,33 +93,43 @@ def read_index(mission='B', remote=False):
 
         df =  pd.read_csv(local_filename, compression='gzip', header=8)
 
-    df['dac'] = np.array([f.split('/')[0] for f in df.file])
     df['wmo'] = np.array([int(f.split('/')[1]) for f in df.file])
-    df['cycle'] = np.array([int(f.split('/')[-1].split('.')[-2].split('_')[-1].replace('D','')) for f in df.file])
+    df['dac'] = np.array([f.split('/')[0] for f in df.file])
+    if mission != 'M' and mission != 'T':
+        df['cycle'] = np.array([int(f.split('/')[-1].split('.')[-2].split('_')[-1].replace('D','')) for f in df.file])
 
     return df
 
-def update_index(ftype=None):
+def update_index(ftype=None, url=url):
     '''
     Function to access FTP server to download Argo metadata and profile global
     index files
     '''
 
-    ftp = ftplib.FTP('ftp.ifremer.fr')
+    ftp = ftplib.FTP(url)
+    url_dir = URL_DIR_DICT[url]
     ftp.login()
-    ftp.cwd('/ifremer/argo/')
+    ftp.cwd(url_dir)
 
     meta  = 'ar_index_global_meta.txt.gz'
+    traj  = 'ar_index_global_traj.txt.gz'
     index = 'ar_index_global_prof.txt.gz'
     bgc   = 'argo_bio-profile_index.txt.gz'
     synth = 'argo_synthetic-profile_index.txt.gz'
 
+
     local_meta = index_path / meta
     if ftype is None or ftype == 'meta':
         lf = open(local_meta, 'wb')
         ftp.retrbinary('RETR ' + meta, lf.write)
         lf.close()
 
+    local_traj = index_path / traj
+    if ftype is None or ftype == 'traj':
+        lf = open(local_traj, 'wb')
+        ftp.retrbinary('RETR ' + traj, lf.write)
+        lf.close()
+
     local_index = index_path / index
     if ftype is None or ftype =='profile' or ftype == 'C':
         lf = open(local_index, 'wb')
@@ -315,7 +353,7 @@ def get_ncep(varname, local_path='./', overwrite=False, years=[2010, 2020]):
         yearlist = years
     elif len(years) == 2:
         yearlist = range(years[0], years[1]+1)
-    elif len(years > 2):
+    else:
         yearlist = years
     
     if varname == 'pres':
@@ -383,7 +421,7 @@ def get_ncep(varname, local_path='./', overwrite=False, years=[2010, 2020]):
 
     return ftp
 
-def get_argo(*args, local_path='./', url='ftp.ifremer.fr', overwrite=False, summary_overwrite=True, ftype=None, mission='CB', mode='RD', __nfiles__=None):
+def get_argo(*args, local_path='./', url=url, overwrite=False, summary_overwrite=True, ftype=None, mission='CB', mode='RD', __nfiles__=None):
     '''
     Function to download all data from a single float, or individual
     profiles
@@ -391,7 +429,7 @@ def get_argo(*args, local_path='./', url='ftp.ifremer.fr', overwrite=False, summ
     Args:
         *args: list of files, floats, or directories
         local_path (optional, str or Path): local directory to save float data, defaults to current directory
-        url (optional, str): url of the GDAC to connect to, defaults to ifremer
+        url (optional, str): url of the GDAC to connect to, defaults to ifremer or the value in the configuration file
         overwrite (optional, bool): whether to overwrite existing local files, default *False*
         ftype (optional, str): can be 'summary' if the user wishes to only download Sprof, traj, meta files, default is *None*
         mission (optional, str): Argo mission, can be 'B' for biogeochemical or 'C' for core, or 'CB' for both, default is 'CB'
@@ -426,12 +464,8 @@ def get_argo(*args, local_path='./', url='ftp.ifremer.fr', overwrite=False, summ
         for init_a in arglist:
             # if its a float number, build ftp paths to floats
             if type(init_a) in [int, float, np.int32, np.int64, np.float32, np.float64]:
-                if url == 'ftp.ifremer.fr':
-                    base_path = '/ifremer/argo/dac'
-                elif url == 'usgodae.org':
-                    base_path = '/pub/outgoing/argo/dac'
-
-                a = '{}/{}/{:d}'.format(base_path, get_dac(init_a), init_a)
+                base_path = URL_DIR_DICT[url]
+                a = (Path(base_path) / 'dac' / get_dac(init_a) / str(init_a)).as_posix()
             else:
                 a = init_a
 
@@ -448,7 +482,7 @@ def get_argo(*args, local_path='./', url='ftp.ifremer.fr', overwrite=False, summ
 
                     # define local location to save file
                     dac_path = local_path / dac
-                    wmo_path = local_path / dac / wmo
+                    wmo_path = dac_path / wmo
                     profile_path = wmo_path / 'profiles'
 
                     # make the directory if it doesn't exist
@@ -489,7 +523,7 @@ def get_argo(*args, local_path='./', url='ftp.ifremer.fr', overwrite=False, summ
                     
                 # define local location to save file
                 dac_path = local_path / dac
-                wmo_path = local_path / dac / wmo
+                wmo_path = dac_path / wmo
 
                 # make the directory if it doesn't exist
                 if not dac_path.is_dir():
@@ -559,7 +593,7 @@ def get_argo(*args, local_path='./', url='ftp.ifremer.fr', overwrite=False, summ
                             ftp.retrbinary('RETR ' + fn, lf.write)
                             lf.close()
 
-    elif len(args) > 1:
+    else:
         local_path = Path(local_path)
 
         dacdir = args[0]
@@ -633,40 +667,33 @@ def load_woa_data(track, param, zlim=(0,1000), local_path='./', verbose=True):
     Function to load WOA18 climatological data for comparison with autonomous
     floats. Data to be interpolated along the provided track (t, lat, lon).
 
-    INPUT:
-              track: array with the columns (SDN, lat, lon)
-              param: requested variable, valid inputs are
-                  T: temperature
-                  S: salinity
-                  O2: dissolved oxygen
-                  O2sat: oxygen percent saturation
-                  NO3: nitrate
-                  Si: silicate
-                  PO4: phosphate
-              zlim: depth bounds (upper, lower), default to (0, 1000)
-              local_path: local directory where WOA files are stored, assumes
-                          current directory if no input
-
-    OUTPUT:
-              xtrack: same as track input, but adjusted lon if the track
-                      crosses the 180/-180 meridian
-              woa_track: list with z, lat, and lon arrays of WOA data
-              data: gridded array of the input variable (month, z, lat, lon)
-
-    AUTHOR:   Christopher Gordon
-              Fisheries and Oceans Canada
-              chris.gordon@dfo-mpo.gc.ca
+    Args:
+        track: array with the columns (SDN, lat, lon)
+        param: requested variable, valid inputs are
+            - T: temperature
+            - S: salinity
+            - O2: dissolved oxygen
+            - O2sat: oxygen percent saturation
+            - NO3: nitrate
+            - Si: silicate
+            - PO4: phosphate
+        zlim: depth bounds (upper, lower), default to (0, 1000)
+        local_path: local directory where WOA files are stored, assumes
+                    current directory if no input
 
-    ACKNOWLEDGEMENT: this code is adapted from the SOCCOM SAGE_O2Argo matlab
-    code, available via https://github.com/SOCCOM-BGCArgo/ARGO_PROCESSING,
-    written by Tanya Maurer & Josh Plant
+    Returns:
+        xtrack: same as track input, but adjusted lon if the track crosses the 180/-180 meridian
+        woa_track: list with z, lat, and lon arrays of WOA data
+        data: gridded array of the input variable (month, z, lat, lon)
 
-    LAST UPDATE: 29-04-2020
+    Acknowledgement: 
+        This code is adapted from the SOCCOM SAGE_O2Argo matlab
+        code, available via https://github.com/SOCCOM-BGCArgo/ARGO_PROCESSING,
+        written by Tanya Maurer & Josh Plant
 
-    CHANGE LOG:
-
-    23-04-2020: changed zlim to optional input argument
-    29-04-2020: switched file/path handling from os module to pathlib
+    Change log:
+        23-04-2020: changed zlim to optional input argument
+        29-04-2020: switched file/path handling from os module to pathlib
     '''
 
     # make local_path a Path() object from a string, account for windows path
@@ -680,6 +707,7 @@ def load_woa_data(track, param, zlim=(0,1000), local_path='./', verbose=True):
         lon_bounds = (np.nanmax(track[lix,2]), np.nanmin(track[~lix,2]))
     else:
         lon_bounds = (np.nanmin(track[:,2]), np.nanmax(track[:,2]))
+        lix = None
     lat_bounds = (np.nanmin(track[:,1]), np.nanmax(track[:,1]))
 
     # set up extraction files, variables
@@ -687,6 +715,11 @@ def load_woa_data(track, param, zlim=(0,1000), local_path='./', verbose=True):
     var_name  = woa_param + '_an'
 
     base_woa_file = 'woa18_{}_{}'.format(woa_ftype, woa_param)
+
+    # assign var names to avoid unbound warnings
+    data, xlon, z_sub, lat_sub, lon_sub = None, None, None, None, None
+    z_ix, lat_ix, lon_ix = None, None, None
+
     # loop through months
     for i in range(12):
         mo = i+1
@@ -741,20 +774,12 @@ def load_ncep_data(track, varname, local_path='./'):
     float in-air data. Data to be interpolated along the provided
     track (t, lat, lon).
 
-    INPUT:
-              track: array with the columns (SDN, lat, lon)
-              local_path: local directory where NCEP files are stored, assumes
-                          current directory if no input
-
-    OUTPUT:
-
-    AUTHOR:   Christopher Gordon
-              Fisheries and Oceans Canada
-              chris.gordon@dfo-mpo.gc.ca
+    Args:
+        track: array with the columns (SDN, lat, lon)
+        local_path: local directory where NCEP files are stored, assumes current directory if no input
 
-    LAST UPDATE: 04-05-2020
+    Returns:
 
-    CHANGE LOG:
     '''
 
     # make local_path a Path() object from a string, account for windows path
@@ -779,6 +804,7 @@ def load_ncep_data(track, varname, local_path='./'):
         lon_bounds = (np.nanmax(track[lix,2]), np.nanmin(track[~lix,2]))
     else:
         lon_bounds = (np.nanmin(track[:,2]), np.nanmax(track[:,2]))
+        lix = None
     lat_bounds = (np.nanmin(track[:,1]), np.nanmax(track[:,1]))
 
     sdn = track[:,0]
@@ -788,6 +814,10 @@ def load_ncep_data(track, varname, local_path='./'):
     if Nyear == 0 and yrs[0] != mdates.datetime.date.today().year:
         Nyear = 1
 
+    # assign var names to avoid unbound warnings
+    data, xlon, ncep_time, landmask, xtrack, ncep_track = None, None, None, None, None, None
+    lat_sub, lon_sub, lat_ix, lon_ix = None, None, None, None
+
     # counter index for going across years
     j = 0
     for n in range(Nyear+1):
@@ -829,7 +859,7 @@ def load_ncep_data(track, varname, local_path='./'):
 
         vdata = nc.variables[varname][:]
         for i in range(len(time)):
-            data_2d =  vdata[i,:,:][:,lon_ix][lat_ix,:]
+            data_2d = vdata[i,:,:][:,lon_ix][lat_ix,:]
             data_2d[landmask] = np.nan
             data[j,:,:] = data_2d
             ncep_time[j] = time[i]
@@ -841,6 +871,28 @@ def load_ncep_data(track, varname, local_path='./'):
 
     return xtrack, ncep_track, data
 
+def copy_netcdf_except(infile, outfile, exclude_vars=[], exclude_dims=[]):
+    '''
+    Copy data from a netCDF file with the exception of dimension and variable
+    names listed in exclude_vars and exclude_dims.
+    '''
+    with Dataset(infile) as src, Dataset(outfile, 'w') as dst:
+        # copy global attributes all at once via dictionary
+        dst.setncatts(src.__dict__)
+        # copy dimensions except for the excluded
+        for name, dimension in src.dimensions.items():
+            if name not in exclude_dims:
+                dst.createDimension(name, (len(dimension) if not dimension.isunlimited() else None))
+        # copy file data except for the excluded
+        for name, variable in src.variables.items():
+            if name not in exclude_vars:
+                x = dst.createVariable(name, variable.datatype, variable.dimensions)
+                # copy variable attributes all at once via dictionary
+                dst[name].setncatts(src[name].__dict__)
+                dst[name][:] = src[name][:]
+    
+    return Dataset(outfile, 'a')
+        
 def append_variable_to_file(fn, *args):
     '''
     Add an arbitrary number of variables (*args) to the existing netcdf file
@@ -848,27 +900,45 @@ def append_variable_to_file(fn, *args):
     fields that can be passed directly to the netCDF file. If the variable
     name already exists, it will overwrite it with the new information.
 
-    Inputs: 
+    Args: 
+        fn: string pointing to netcdf (.nc) file to be appended
+        *args: arbitrary number of python dicts with all required fields to create
+        or overwrite a new netcdf variable. Example: 
+
+        new_var = dict(
+            name='MY_NEW_VARIABLE',     # variable name, can be new or existing
+            datatype=np.float64,        # variable datatype, can be np datatype or string
+            dimensions=('N', 'M'),      # will be created with warning if they do not exist
+            data=data_arr,              # the data
+            long_name='The new variable',
+            standard_name='my_new_var',
+            units='degree_celsius',
+            valid_min=0,
+            valid_max=1e9,
+            resolution=0.001,
+            comment='Added by John Doe on Sept 20, 2019'
+        )
+    '''
 
-    fn: string pointing to netcdf (.nc) file to be appended
+    nc = Dataset(fn, 'a')
+    
+    for new_var in args:
+        name = new_var.pop('name')
+        data = new_var.pop('data')
+        nc.createVariable(name, new_var.pop('datatype'), new_var.pop('dimensions'))
+        nc[name].setncatts(new_var)
+        nc[name][:] = data
 
-    *args: arbitrary number of python dicts with all required fields to create
-    or overwrite a new netcdf variable. Example: 
+    return nc
 
-    new_var = dict(
-        name='MY_NEW_VARIABLE',     # variable name, can be new or existing
-        dimensions=('N', 'M'),      # note these dims must already exist in the nc file
-        long_name='The new variable',
-        standard_name='my_new_var',
-        units='degree_celsius',
-        valid_min=0,
-        valid_max=1e9,
-        resolution=0.001,
-        comment='Added by John Doe on Sept 20, 2019'
-    )
-    '''
+def generate_standard_comments():
 
+    return
 
-    nc = Dataset(fn, 'a')
+def append_scienfitic_calib_information(nc_var, str_to_append):
+
+    return
+
+def str_to_nc(s):
 
-    return nc
\ No newline at end of file
+    return
\ No newline at end of file
diff --git a/build/lib/bgcArgoDMQC/lut.py b/build/lib/bgcArgoDMQC/lut.py
new file mode 100644
index 0000000..36d8775
--- /dev/null
+++ b/build/lib/bgcArgoDMQC/lut.py
@@ -0,0 +1,3119 @@
+import numpy as np
+
+global lut
+lut = np.array([[1.0000000e+02,  0.0000000e+00,  2.0000000e+00,  4.0000000e+00,
+   6.0000000e+00,  8.0000000e+00,  1.0000000e+01,  1.2000000e+01,
+   1.4000000e+01,  1.6000000e+01,  1.8000000e+01,  2.0000000e+01,
+   2.2000000e+01,  2.4000000e+01,  2.6000000e+01,  2.8000000e+01,
+   3.0000000e+01,  3.2000000e+01,  3.4000000e+01,  3.6000000e+01,
+   3.8000000e+01,  4.0000000e+01,  4.2000000e+01,  4.4000000e+01,
+   4.6000000e+01,  4.8000000e+01,  5.0000000e+01,  5.2000000e+01,
+   5.4000000e+01,  5.6000000e+01,  5.8000000e+01,  6.0000000e+01,
+   6.2000000e+01,  6.4000000e+01,  6.6000000e+01,  6.8000000e+01,
+   7.0000000e+01,  7.2000000e+01,  7.4000000e+01,  7.6000000e+01,
+   7.8000000e+01,  8.0000000e+01,  8.2000000e+01,  8.4000000e+01,
+   8.6000000e+01,  8.8000000e+01,  9.0000000e+01,  9.2000000e+01,
+   9.4000000e+01,  9.6000000e+01,  9.8000000e+01,  1.0000000e+02,
+   1.0200000e+02,  1.0400000e+02,  1.0600000e+02,  1.0800000e+02,
+   1.1000000e+02,  1.1200000e+02,  1.1400000e+02,  1.1600000e+02,
+   1.1800000e+02,  1.2000000e+02,  1.2200000e+02,  1.2400000e+02,
+   1.2600000e+02,  1.2800000e+02,  1.3000000e+02,  1.3200000e+02,
+   1.3400000e+02,  1.3600000e+02,  1.3800000e+02,  1.4000000e+02,
+   1.4200000e+02,  1.4400000e+02,  1.4600000e+02,  1.4800000e+02,
+   1.5000000e+02,  1.5200000e+02,  1.5400000e+02,  1.5600000e+02,
+   1.5800000e+02,  1.6000000e+02,  1.6200000e+02,  1.6400000e+02,
+   1.6600000e+02,  1.6800000e+02,  1.7000000e+02,  1.7200000e+02,
+   1.7400000e+02,  1.7600000e+02,  1.7800000e+02,  1.8000000e+02,
+   1.8200000e+02,  1.8400000e+02,  1.8600000e+02,  1.8800000e+02,
+   1.9000000e+02,  1.9200000e+02,  1.9400000e+02,  1.9600000e+02,
+   1.9800000e+02,  2.0000000e+02,  2.0200000e+02,  2.0400000e+02,
+   2.0600000e+02,  2.0800000e+02,  2.1000000e+02,  2.1200000e+02,
+   2.1400000e+02,  2.1600000e+02,  2.1800000e+02,  2.2000000e+02,
+   2.2200000e+02,  2.2400000e+02,  2.2600000e+02,  2.2800000e+02,
+   2.3000000e+02,  2.3200000e+02,  2.3400000e+02,  2.3600000e+02,
+   2.3800000e+02,  2.4000000e+02,  2.4200000e+02,  2.4400000e+02,
+   2.4600000e+02,  2.4800000e+02,  2.5000000e+02,  2.5500000e+02,
+   2.6000000e+02,  2.6500000e+02,  2.7000000e+02,  2.7500000e+02,
+   2.8000000e+02,  2.8500000e+02,  2.9000000e+02,  2.9500000e+02,
+   3.0000000e+02,  3.0500000e+02,  3.1000000e+02,  3.1500000e+02,
+   3.2000000e+02,  3.2500000e+02,  3.3000000e+02,  3.3500000e+02,
+   3.4000000e+02,  3.4500000e+02,  3.5000000e+02,  3.5500000e+02,
+   3.6000000e+02,  3.6500000e+02,  3.7000000e+02,  3.7500000e+02,
+   3.8000000e+02,  3.8500000e+02,  3.9000000e+02,  3.9500000e+02,
+   4.0000000e+02,  4.0500000e+02,  4.1000000e+02,  4.1500000e+02,
+   4.2000000e+02,  4.2500000e+02,  4.3000000e+02,  4.3500000e+02,
+   4.4000000e+02,  4.4500000e+02,  4.5000000e+02,  4.5500000e+02,
+   4.6000000e+02,  4.6500000e+02,  4.7000000e+02,  4.7500000e+02,
+   4.8000000e+02,  4.8500000e+02,  4.9000000e+02,  4.9500000e+02,
+   5.0000000e+02,  5.0500000e+02,  5.1000000e+02,  5.1500000e+02,
+   5.2000000e+02,  5.2500000e+02,  5.3000000e+02,  5.3500000e+02,
+   5.4000000e+02,  5.4500000e+02,  5.5000000e+02,  5.5500000e+02,
+   5.6000000e+02,  5.6500000e+02,  5.7000000e+02,  5.7500000e+02,
+   5.8000000e+02,  5.8500000e+02,  5.9000000e+02,  5.9500000e+02,
+   6.0000000e+02,  6.0500000e+02,  6.1000000e+02,  6.1500000e+02,
+   6.2000000e+02,  6.2500000e+02,  6.3000000e+02,  6.3500000e+02,
+   6.4000000e+02,  6.4500000e+02,  6.5000000e+02,  6.5500000e+02,
+   6.6000000e+02,  6.6500000e+02,  6.7000000e+02,  6.7500000e+02,
+   6.8000000e+02,  6.8500000e+02,  6.9000000e+02,  6.9500000e+02,
+   7.0000000e+02,  7.0500000e+02,  7.1000000e+02,  7.1500000e+02,
+   7.2000000e+02,  7.2500000e+02,  7.3000000e+02,  7.3500000e+02,
+   7.4000000e+02,  7.4500000e+02,  7.5000000e+02,  7.6000000e+02,
+   7.7000000e+02,  7.8000000e+02,  7.9000000e+02,  8.0000000e+02,
+   8.1000000e+02,  8.2000000e+02,  8.3000000e+02,  8.4000000e+02,
+   8.5000000e+02,  8.6000000e+02,  8.7000000e+02,  8.8000000e+02,
+   8.9000000e+02,  9.0000000e+02,  9.1000000e+02,  9.2000000e+02,
+   9.3000000e+02,  9.4000000e+02,  9.5000000e+02,  9.6000000e+02,
+   9.7000000e+02,  9.8000000e+02,  9.9000000e+02,  1.0000000e+03,
+   1.0100000e+03,  1.0200000e+03,  1.0300000e+03,  1.0400000e+03,
+   1.0500000e+03,  1.0600000e+03,  1.0700000e+03,  1.0800000e+03,
+   1.0900000e+03,  1.1000000e+03,  1.1100000e+03,  1.1200000e+03,
+   1.1300000e+03,  1.1400000e+03,  1.1500000e+03,  1.1600000e+03,
+   1.1700000e+03,  1.1800000e+03,  1.1900000e+03,  1.2000000e+03,
+   1.2100000e+03,  1.2200000e+03,  1.2300000e+03,  1.2400000e+03,
+   1.2500000e+03,  1.2600000e+03,  1.2700000e+03,  1.2800000e+03,
+   1.2900000e+03,  1.3000000e+03,  1.3100000e+03,  1.3200000e+03,
+   1.3300000e+03,  1.3400000e+03,  1.3500000e+03,  1.3600000e+03,
+   1.3700000e+03,  1.3800000e+03,  1.3900000e+03,  1.4000000e+03,
+   1.4100000e+03,  1.4200000e+03,  1.4300000e+03,  1.4400000e+03,
+   1.4500000e+03,  1.4600000e+03,  1.4700000e+03,  1.4800000e+03,
+   1.4900000e+03,  1.5000000e+03,],
+ [-2.0000000e+00,  5.7302937e+00,  7.0901023e+00,  8.5693947e+00,
+   1.0088733e+01,  1.1627610e+01,  1.3177927e+01,  1.4736394e+01,
+   1.6301206e+01,  1.7871778e+01,  1.9446238e+01,  2.1024465e+01,
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+   3.4598009e+02,  3.4883104e+02,  3.5168925e+02,  3.5742773e+02,
+   3.6319571e+02,  3.6899293e+02,  3.7481969e+02,  3.8067589e+02,
+   3.8656153e+02,  3.9247680e+02,  3.9842141e+02,  4.0439570e+02,
+   4.1039952e+02,  4.1643290e+02,  4.2249586e+02,  4.2858858e+02,
+   4.3471075e+02,  4.4086273e+02,  4.4704436e+02,  4.5325566e+02,
+   4.5949665e+02,  4.6576754e+02,  4.7206815e+02,  4.7839831e+02,
+   4.8475842e+02,  4.9114851e+02,  4.9756819e+02,  5.0401794e+02,
+   5.1049723e+02,  5.1700662e+02,  5.2354588e+02,  5.3011503e+02,
+   5.3671409e+02,  5.4334328e+02,  5.5000240e+02,  5.5669124e+02,
+   5.6341023e+02,  5.7015915e+02,  5.7693823e+02,  5.8374700e+02,
+   5.9058594e+02,  5.9745458e+02,  6.0435340e+02,  6.1128218e+02,
+   6.1824093e+02,  6.2522991e+02,  6.3224864e+02,  6.3929763e+02,
+   6.4637639e+02,  6.5348543e+02,  6.6062451e+02,  6.6779390e+02,
+   6.7499312e+02,  6.8222237e+02,  6.8948198e+02,  6.9677168e+02,
+   7.0409148e+02,  7.1144139e+02,  7.1882140e+02,  7.2623154e+02,
+   7.3367208e+02,  7.4114230e+02,  7.4864308e+02,  7.5617402e+02,
+   7.6373510e+02,  7.7132635e+02,  7.7894776e+02,  7.8659934e+02,
+   7.9428138e+02,  8.0199361e+02,  8.0973574e+02,  8.1750836e+02,
+   8.2531117e+02,  8.3314419e+02,  8.4100772e+02,  8.4890117e+02,
+   8.5682514e+02,  8.6477904e+02,],],)
\ No newline at end of file
diff --git a/build/lib/bgcArgoDMQC/unit.py b/build/lib/bgcArgoDMQC/unit.py
index 62478d6..10ff521 100644
--- a/build/lib/bgcArgoDMQC/unit.py
+++ b/build/lib/bgcArgoDMQC/unit.py
@@ -1,21 +1,9 @@
 import warnings
 
 import numpy as np
+import gsw
 
-# soft attempt to load gsw, but allow for seawater as well
-try: 
-    import gsw
-    flagSA = True
-except:
-    try:
-        # if this also fails, just load gsw to throw the error
-        from seawater import pden
-        flagSA = False
-        warnings.warn('gsw package for thermodynamic equations of seawater not installed, attempting to load seawater package, however seawater is deprecated in favour of gsw-python, see https://teos-10.github.io/GSW-Python/\n')
-    except:
-        import gsw
-
-def oxy_sol(S, T, unit='micromole/kg'):
+def oxy_sol(S, T, PDEN, a4330=True):
     '''
     Calculate oxygen saturation concentration in seawater as a function of
     S & T, in equilibrium with standard coponsition moist air at 1atm total
@@ -23,46 +11,32 @@ def oxy_sol(S, T, unit='micromole/kg'):
     of Benson & Krause in table 1, as used in Sarmiento & Gruber's "Ocean
     Biogeochemical Dynamics" ch. 3, p. 81, table 3.2.4.
     
-    INPUT:
-              S: salinity, psu
-              T: temperature, deg C
-              unit: micromole/kg or millimole/m3, default if micromole/kg
-    
-    OUTPUT:
-              O2sol: oxygen solubility, unit same as input unit
-    
-    AUTHOR:   Christopher Gordon
-              Fisheries and Oceans Canada
-              chris.gordon@dfo-mpo.gc.ca
-    
-    ACKNOWLEDGEMENT: this code is adapted from the SOCCOM SAGE_O2Argo matlab
-    code, available via https://github.com/SOCCOM-BGCArgo/ARGO_PROCESSING,
-    written by Josh Plant
-    
-    LAST UPDATE: 21-04-2020
-    
-    CHANGE LOG:
-    '''
-
-    # check for improper units
-    if unit != 'micromole/kg' and unit != 'millimole/m3':
-        raise ValueError('Unrecognized unit string - valid units are ''micromole/kg'' or ''millimole/m3''.')
-
-    if unit == 'micromole/kg':
-        A = [3.80369, -9.86643e-2, 5.10006, 4.17887, 3.20291, 5.80871]
-        B = [-9.51519e-3, -1.13864e-2, -7.70028e-3, -7.01577e-3]
-        C = -2.75915e-7
+    Args:
+        S (float or array-like): salinity, psu
+        T (float or array-like): temperature, deg C
+        a4330 (bool): True or False for Aanderaa 4330 optode
 
-    elif unit == 'millimole/m3':
-        A = [3.88767, -0.256847, 4.94457, 4.05010, 3.22014, 2.00907]
-        B = [-8.17083e-3, -1.03410e-2, -7.37614e-3, -6.24523e-3]
+    Returns:
+        O2sol (float or array-like): oxygen solubility
+    '''
+    
+    O2_volume =  22.3916
+    if a4330:
+        A = [1.71069, 0.978188, 4.80299, 3.99063, 3.22400, 2.00856] # Temperature coeff
+        B = [-4.29155e-3, -6.90358e-3, -6.93498e-3, -6.24097e-3] # Salinity coeff
+        C = -3.11680e-7
+        factor = 1000/O2_volume
+    else:
+        A = [3.88767, -0.256847, 4.94457, 4.05010, 3.22014, 2.00907] # Temperature coeff
+        B = [-8.17083e-3, -1.03410e-2, -7.37614e-3, -6.24523e-3] # Salinity coeff
         C = -4.88682e-7
+        factor = 44.614 # listed in argo cookbook
 
     # Scaled temperature
-    Ts = np.log((298.15 - T)/(273.15 + T));
+    Ts = np.log((298.15 - T)/(273.15 + T))
     L = np.polyval(A,Ts) + S*np.polyval(B,Ts) + C*S**2
 
-    O2sol = np.exp(L)
+    O2sol = factor*np.exp(L)*1000/PDEN
 
     return O2sol
 
@@ -70,20 +44,13 @@ def pH2O(T, S=0, unit='Pa'):
     '''
     Calculate vapor pressure of water
     
-    INPUT:
-              T: temperature, deg C
-              S: salinity, only necessary for mbar unit
-    
-    OUTPUT:
-              vapor_pressure: vapor pressure of water, Pa or mbar
-    
-    AUTHOR:   Christopher Gordon
-              Fisheries and Oceans Canada
-              chris.gordon@dfo-mpo.gc.ca
-    
-    LAST UPDATE: 06-05-2020
-    
-    CHANGE LOG:
+    Args:
+        T (float or array-like): temperature, deg C
+        S (optional, float or array-like): salinity, only necessary for mbar unit, default 0
+        unit (optional, str): "Pa" or "mbar" for unit of vapor pressure, defauly "Pa"
+
+    Returns:
+        vapor_pressure: vapor pressure of water, Pa or mbar
     '''
     
     # temperature in kelvin
@@ -94,7 +61,7 @@ def pH2O(T, S=0, unit='Pa'):
         vapor_pressure = np.exp(52.57 - (6690.9/Tk) - 4.681*np.log(Tk))
     elif unit == 'mbar':
         # SCOR WG 142
-        vapor_pressure = 1013.25 * (np.exp(24.4543 - (67.4509*(100/Tk))) - 4.8489*np.log(((Tk/100)) - 0.000544*S))
+        vapor_pressure = 1013.25 * (np.exp(24.4543 - (67.4509*(100/Tk)) - 4.8489*np.log(Tk/100) - 0.000544*S))
     else:
         raise ValueError('Invalid unit input of {}, must be one of "Pa" or "mbar"'.format(unit))
 
@@ -152,15 +119,7 @@ def umol_per_sw_to_mmol_per_L(doxy, S, T, P, Pref=0, lat=None, lon=None):
         umol_L_conc (float or array-like): dissolved oxygen concentration in umol L-1 (or equivalently mmol m-3)
     '''
 
-    if flagSA:
-        if lat is None and lon is None:
-            pot_density = gsw.pot_rho_t_exact(S, T, P, Pref)
-            warnings.warn('No coordinate information required, proceeding with calculation using practical salinity instead of absolute salinity')
-        else:
-            pot_density = gsw.pot_rho_t_exact(gsw.SA_from_SP(S, P, lon, lat), T, P, Pref)
-    else:
-        pot_density = pden(S, T, P, pr=Pref)
-
+    pot_density = gsw.pot_rho_t_exact(gsw.SA_from_SP(S, P, lon, lat), T, P, Pref)
     umol_L_conc = 1000*doxy / pot_density
 
     return umol_L_conc
@@ -278,6 +237,6 @@ def pO2_to_doxy(pO2, S, T, P=0):
     Vm      = 0.317 # molar volume of O2 in m3 mol-1 Pa dbar-1 (Enns et al. 1965)
     R       = 8.314 # universal gas constant in J mol-1 K-1
 
-    O2conc  = pO2/(xO2*(1013.25 - pH2Osat))/(Tcorr*Scorr)*np.exp(Vm*P/(R*Tk))
+    O2conc  = pO2/(xO2*(1013.25 - pH2Osat))*(Tcorr*Scorr)/np.exp(Vm*P/(R*Tk))
 
     return O2conc
\ No newline at end of file
diff --git a/build/lib/bgcArgoDMQC/util.py b/build/lib/bgcArgoDMQC/util.py
index 2e17997..3730b60 100644
--- a/build/lib/bgcArgoDMQC/util.py
+++ b/build/lib/bgcArgoDMQC/util.py
@@ -1,4 +1,5 @@
 import sys
+from pathlib import Path
 import numpy as np
 from netCDF4 import Dataset
 
@@ -235,6 +236,9 @@ def read_gain_value(fn, verbose=True):
             equation = eqs[ix]
             comment = comms[ix]
     
+    else:
+        raise ValueError('Cannot parse calibration info based on dimensions N_PROF={} and N_CALIB={}'.format(nprof, ncalib))
+    
 
     return G, equation, comment
 
@@ -382,4 +386,61 @@ def haversine(c1, c2):
 
 def cycle_from_time(time, t_arr, c_arr):
     ix = np.abs(t_arr - time) == np.nanmin(np.abs(t_arr - time))
-    return c_arr[ix][0]
\ No newline at end of file
+    return c_arr[ix][0]
+
+def get_vars(files):
+
+    nc = Dataset(Path(files[0]), 'r')
+    varnames = set(nc.variables.keys())
+    nc.close()
+
+    for fn in files:
+        nc = Dataset(Path(fn), 'r')
+        varnames = varnames.intersection(nc.variables.keys())
+        nc.close()
+
+    varnames = list(varnames)
+
+    return varnames
+
+def read_ncstr(arr):
+    decode_str = np.array([f.decode('utf-8') for f in arr])
+    out = ''.join(decode_str)
+
+    return out.strip()
+
+def read_qc(flags):
+
+    decode_flags = np.array([f.decode('utf-8') for f in flags])
+    decode_flags[decode_flags == ' '] = '4'
+
+    out_flags = np.array([int(f) for f in decode_flags])
+
+    return out_flags
+
+def get_worst_flag(*args):
+    out_flags = np.ones(args[0].shape)
+
+    if len(args) == 1:
+        out_flags = args[0]
+    else:
+        # make an array where all data marked as good
+        out_flags = np.ones(args[0].shape)
+        # loop through input flags
+        for flags in args:
+            # loop through each datapoint flag
+            for i,f in enumerate(flags):
+                if f > out_flags[i] and f <= 4:
+                    out_flags[i] = f
+                elif f in [5,8] and out_flags[i] <= 2:
+                    out_flags[i] = f
+                elif f == 9:
+                    out_flags[i] = 9
+        
+    return out_flags
+
+def get_parameter_index(parameter_array, parameter):
+    str_arr = np.array([read_ncstr(s) for s in parameter_array])
+    index = np.where(str_arr == parameter)[0]
+
+    return index
\ No newline at end of file
diff --git a/dist/bgcArgoDMQC-0.2.10.tar.gz b/dist/bgcArgoDMQC-0.2.11-py3-none-any.whl
similarity index 77%
rename from dist/bgcArgoDMQC-0.2.10.tar.gz
rename to dist/bgcArgoDMQC-0.2.11-py3-none-any.whl
index d99495d..ef6b49f 100644
Binary files a/dist/bgcArgoDMQC-0.2.10.tar.gz and b/dist/bgcArgoDMQC-0.2.11-py3-none-any.whl differ
diff --git a/dist/bgcArgoDMQC-0.2.10-py3-none-any.whl b/dist/bgcArgoDMQC-0.2.11.tar.gz
similarity index 77%
rename from dist/bgcArgoDMQC-0.2.10-py3-none-any.whl
rename to dist/bgcArgoDMQC-0.2.11.tar.gz
index a518b2a..bb63693 100644
Binary files a/dist/bgcArgoDMQC-0.2.10-py3-none-any.whl and b/dist/bgcArgoDMQC-0.2.11.tar.gz differ
diff --git a/setup.py b/setup.py
index c37b6b4..d26bba9 100644
--- a/setup.py
+++ b/setup.py
@@ -8,7 +8,7 @@
 
 setuptools.setup(
     name='bgcArgoDMQC',
-    version='0.2.10',
+    version='0.2.11',
     license='The MIT License (MIT)',
     author_email='chris.gordon@dfo-mpo.gc.ca',
     description='A python library for quality control of BGC-Argo data',