comments updated

.gitignore

@@ -2,4 +2,5 @@
 #*
 workbench/
 *egg-info/
-build*
+build*
+__pycache__*

cloudynight/__init__.py

@@ -17,36 +17,38 @@ class ConfExample():
     """Allsky camera configuration class."""
 
     def __init__(self):
-
-        # define base directory structure
-        # each directory gets `AllskyCamera.night` appended
-        # [here, directories are defined relative to the scripts/ directory]
 
+        # directory structure setup (note that data actually sits in
+        # subdirectories that are, e.g., separated by nights)
+
+        # define base directory
         self.DIR_BASE = os.path.join(
             ' ', *os.path.abspath(__file__).split('/')[:-2]).strip()
         # location of module base (for example data)
 
+        # raw data directory root
         self.DIR_RAW = os.path.join(self.DIR_BASE, 'example_data')
 
+        # archive root (will contain thumbnail images for webapp)
         self.DIR_ARCHIVE = os.path.join(self.DIR_BASE, 'workbench')
 
         # data directory location on host machine (where to pull FITS files from)
         self.CAMHOST_NAME = ''
         self.CAMHOST_BASEDIR = ''
 
-        # FITS file prefix and suffix used by allsky images
+        # FITS file prefix and suffix of allsky images
         self.FITS_PREFIX = ''
         self.FITS_SUFFIX = 'fits.bz2'
 
         # SEP parameters
-        self.SEP_SIGMA = 1.5
-        self.SEP_MAXFLAG = 7
-        self.SEP_MINAREA = 3
-        self.SEP_DEBLENDN = 32
-        self.SEP_DEBLENDV = 0.005
-        self.SEP_BKGBOXSIZE = 15
-        self.SEP_BKGXRANGE = 3
-        self.SEP_BKGYRANGE = 5
+        self.SEP_SIGMA = 1.5       # sigma threshold
+        self.SEP_MAXFLAG = 7       # maximum source flag considered
+        self.SEP_MINAREA = 3       # minimum number of pixels per source
+        self.SEP_DEBLENDN = 32     # number of deblending steps
+        self.SEP_DEBLENDV = 0.005  # deblending parameter
+        self.SEP_BKGBOXSIZE = 15   # edge size of box for deriving background
+        self.SEP_BKGXRANGE = 3     # number of background boxes in x
+        self.SEP_BKGYRANGE = 5     # number of background boxes in y
 
         # max solar elevation for processing (deg)
         self.MAX_SOLAR_ELEVATION = -6;  
@@ -69,15 +71,16 @@ def __init__(self):
         self.MASK_FILENAME = os.path.abspath(os.path.join(self.DIR_RAW,
                                                        'mask.fits'))
 
-        # database URL and credentials
+        # database URL and credentials for database user `writer`
         self.DB_URL = 'http://127.0.0.1:8000/'
         self.DB_USER = 'writer'
         self.DB_PWD = 'writecloud'
 
-        # url for retrieving training data
+        # uris for retrieving data
         self.TRAINDATA_URL = 'http://127.0.0.1:8000/getAllLabeled/'
         self.UNTRAINDATA_URL = 'http://127.0.0.1:8000/getAllUnlabeled/'
 
+        # lightGBM model optimal parameters
         self.LGBMODEL_PARAMETERS = {
             'max_depth': 5,
             'n_estimators': 500,
@@ -87,6 +90,7 @@ def __init__(self):
             'reg_alpha': 10,
             'reg_lambda': 100}
 
+        # lightgbm model parameter ranges for randomized search
         self.LGBMODEL_PARAMETER_DISTRIBUTIONS = {
             'max_depth': randint(low=3, high=47),
             'n_estimators': randint(low=100, high=1400),
@@ -97,6 +101,7 @@ def __init__(self):
             'reg_alpha': [1, 5, 10, 50, 100],
             'reg_lambda': [1, 5, 10, 50, 100, 500, 1000]}
 
+        # location of trained model file
         self.LGBMODEL_FILE = os.path.join(self.DIR_ARCHIVE,
                                              'lightgbm.pickle')