Merge branch '#16' of https://github.com/LSSTDESC/BlendingToolKit into …

…#16

btk/utils.py

@@ -35,12 +35,21 @@ def get_deblended_images(self, data, index):
 
 
 class Stack_params(measure.Measurement_params):
-    min_pix = 1
-    bkg_bin_size = 32
-    thr_value = 5
-    psf_stamp_size = 41
+    """Class with functions that describe how LSST science pipeline can
+    perform measurements on the input data."""
+    min_pix = 1  # Minimum size in pixels to be considered a source
+    bkg_bin_size = 32  # Binning size of the local background
+    thr_value = 5  # SNR threshold for the detection
+    psf_stamp_size = 41  # size of pstamp to draw PSF on
 
     def get_psf_sky(self, obs_cond):
+        """Returns postage stamp image of the PSF and mean background sky
+        level value saved in the input obs_cond class
+        Args:
+            obs_cond:`descwl.survey.Survey` class describing observing
+                      conditions.
+
+        """
         mean_sky_level = obs_cond.mean_sky_level
         psf = obs_cond.psf_model
         psf_image = psf.drawImage(
@@ -51,7 +60,15 @@ def get_psf_sky(self, obs_cond):
 
     def make_measurement(self, data, index):
         """Perform detection, deblending and measurement on the i band image of
-        the blend image for input index in the batch.
+        the blend for input index entry in the batch.
+
+        Args:
+            data: Dictionary with blend images, isolated object images, blend
+                  catalog, and observing conditions.
+            index: Position of the blend to measure in the batch.
+
+        Returns:
+            astropy.Table of the measurement results.
          """
         image_array = data['blend_images'][index, :, :, 3].astype(np.float32)
         psf_image, mean_sky_level = self.get_psf_sky(
@@ -135,14 +152,26 @@ def run_stack(image_array, variance_array, psf_array,
 
 
 class Scarlet_params(measure.Measurement_params):
-    iters = 200
-    e_rel = .015
+    """Class with functions that describe how scarlet should deblend images in
+    the input data"""
+    iters = 200  # Maximum number of iterations for scarlet to run
+    e_rel = .015  # Relative error for convergence
     detect_centers = True
 
     def make_measurement(self, data=None, index=None):
         return None
 
     def get_centers(self, image):
+        """Returns centers from SEP detection on the band averaged mean of the
+        input image.
+
+        Args:
+            image: Numpy array of multi-band image to run scarlet on
+                    [Number of bands, height, width].
+
+        Returns:
+            Array of x and y coordinate of centroids of objects in the image.
+        """
         sep = __import__('sep')
         detect = image.mean(axis=0)  # simple average for detection
         bkg = sep.Background(detect)
@@ -159,7 +188,8 @@ def scarlet_initialize(self, images, peaks,
             peaks: Array of x and y coordinate of centroids of objects in
                    the image [number of sources, 2].
             bg_rms: Background RMS value of the images [Number of bands]
-        Returns
+
+        Returns:
             blend: scarlet.Blend object for the initialized sources
             rejected_sources: list of sources (if any) that scarlet was
                               unable to initialize the image with.
@@ -184,18 +214,19 @@ def get_deblended_images(self, data, index):
         """
         Deblend input images with scarlet
         Args:
-        images: Numpy array of multi-band image to run scarlet on
-               [Number of bands, height, width].
-        peaks: x and y coordinate of centroids of objects in the image.
-               [number of sources, 2]
-        bg_rms: Background RMS value of the images [Number of bands]
-        iters: Maximum number of iterations if scarlet doesn't converge
-               (Default: 200).
-        e_rel: Relative error for convergence (Default: 0.015)
-        Returns
-        blend: scarlet.Blend object for the initialized sources
-        rejected_sources: list of sources (if any) that scarlet was
-        unable to initialize the image with.
+            images: Numpy array of multi-band image to run scarlet on
+                   [Number of bands, height, width].
+            peaks: x and y coordinate of centroids of objects in the image.
+                   [number of sources, 2]
+            bg_rms: Background RMS value of the images [Number of bands]
+            iters: Maximum number of iterations if scarlet doesn't converge
+                   (Default: 200).
+            e_rel: Relative error for convergence (Default: 0.015)
+
+        Returns:
+            blend: scarlet.Blend object for the initialized sources
+            rejected_sources: list of sources (if any) that scarlet was
+            unable to initialize the image with.
         """
         images = np.transpose(data['blend_images'][index], axes=(2, 0, 1))
         blend_cat = data['blend_list'][index]
@@ -223,6 +254,7 @@ def make_true_seg_map(image, threshold):
         image: Image to estimate segmentation map of
         threshold: Pixels above this threshold are marked as belonging to
                    segmentation map
+
     Returns:
         Boolean segmentation map of the image
     """
@@ -338,9 +370,9 @@ def get_centers(self, image):
         is done on input image with SEP.
         Args:
             image: Image (single band) of galaxy to perform measurement on.
+
         Returns:
                 centers: x and y coordinates of detected  centroids
-
         """
         # set detection threshold to 5 times std of image
         threshold = 5*np.std(image)
@@ -398,7 +430,7 @@ def __init__(self, *args, **kwargs):
     """
 
     def get_detections(self):
-        """Returns blend catalog and detection catalog for detction performed
+        """Returns blend catalog and detection catalog for detection performed
 
         Returns:
             Results of the detection algorithm are returned as: