Method documentation updated

puppeteer/__init__.py

@@ -4,7 +4,7 @@
     classes that will let any user program the light modulator: SetupDevice()
     and SetProtocol().\n
 
-    SetupDevice(self, panel_type="rgb", default_filter="No_Filter")\n\t
+    SetupDevice(panel_type="rgb", default_filter="No_Filter")\n\t
         Initialise the light modulator and set the routines needed to control
         the hardware,ranging from light modulation, filte#r selection, 
         temperature control and or image acquisition. This library also provides

puppeteer/_dataparser/__init__.py

@@ -1,5 +1,28 @@
 """
-asd
+    The `dataparser' module is responsible for collecting biological data from
+    photographs stored in the light modulator. Currently this step is performed
+    on the GUI side, but the light modulator can also capable to perform this
+    task.\n
+    
+    PlateSniffer(dir_info, plate_reference, filter, debug=False)\n\t
+        Routine to detect well location from image data. Using maximally stable
+        extreme regions (MSRE) as core blob detection algorithm, this routine
+        will detect the centre of each well of a microtitre plate. NOTE THAT
+        ONLY THE X,Y COORDINATES OF THE WELLS WILL BE RETURNED, AND NOT THEIR
+        SIZE. To reduce the bandwidth used to transfer data, it crops the raw
+        image and re-calculates well locations. The method `detect_wells'
+        requires a directory structure (`dir_info'), plate of reference, and
+        filter as inputs. There's an optional `debug' flag, which defaults to
+        false.\n
+    
+    PlateParser(dir_info, wells, filter, row_labels, col_labels, img_limits,
+                time, debug=False)\n\t
+        Collect biolotical data from photographs. `wells' is an object that
+        contains wells (x,y) coordinates. The objects `row_labels' and
+        `col_labels' are used to classify the well location following
+        standardised notation for mitrotitre plate well locations
+        (i.e. A1, A2,...,H12 for a 96-well microtitre plate). IMPORTANT: THIS
+        METHOD IS CAPABLE OF USING MULTIPLE CORES TO ACCELERATE DATA PROCESSING.
 """
 from .plate_sniffer import detect_wells as wells_id
 from .plate_reader import PlateParser as read_plate

puppeteer/_dataparser/plate_reader.py

@@ -72,7 +72,11 @@ class PlateParser:
     def _multicore_reader(self, i, img_data_path, img_list, blank_data_path,
                           blanks_list, radius, pixels_per_well, row_labels,
                           col_labels, debug):
-        """ DocString. """
+        """
+            This routine exploits the presence of multiple cores to accelerate
+            image processing. Each file in `img_list' gets blank corrected and
+            image data is stored using `wells' cached coordinates.
+        """
         img_filename = img_list[i]
         ref_image = blanks_list[i]
         img = cv2.imread(img_data_path + img_filename, cv2.IMREAD_COLOR)
@@ -121,7 +125,10 @@ class PlateParser:
 
     def _read_wells(self, dir_info, wells, flt, row_labels,
                     col_labels, img_limits, time, debug):
-        """ Retrieve data distribution from images. """
+        """
+            Retrieve data from images using precalculated `wells' coordinates,
+            and cropped using precalculated `img_limits'.
+        """
         img_data_path = dir_info.root_path + dir_info.protocol_path +\
                          dir_info.img_data_path + flt + "/"
         img_pattern = "_" + str(time) + "s_"

puppeteer/_dataparser/plate_sniffer.py

@@ -59,7 +59,7 @@ def detect_wells(dir_info, plate_reference, flt, debug=False):
 
     # Adjust well position in a smaller image to be used downstream (FIXME: Needed? [YES]).
     img_c = crop_img(img, img_limits)  # Optimise perf.
-    ## Maximally stable extremal region (MSER) blob detector.
+    ## Re-calculate well locations in newer, smaller image.
     # MSER = cv2.MSER_create()
     # blobs = MSER.detect(img_c)
     # wells = [b for b in blobs if not _filter_blobs(b, blobs)]