added descriptions for parmeters to output file

ideas.md

@@ -1,4 +1,4 @@
-Furhter Ideas:
+Further Ideas:
 
 - color the roi
 - create a movie

mtor/dataproc.py

@@ -382,6 +382,13 @@ def save_data(data_frame, selected_df, extremas_df, parameters):
         k: [v] for k, v in parameters.items() if k not in ignore_parameters
     }
     tmp_setings_df = pandas.DataFrame(tmp_parameters).T
+    all_descriptions = parameters.get_descriptions()
+    matched_descritions = {
+        key: value
+        for key, value in all_descriptions.items()
+        if key in tmp_parameters
+    }
+    tmp_setings_df["Descriptions"] = pandas.Series(matched_descritions)
     tmp_setings_df.to_excel(writer, sheet_name="parameters")
 
     writer.save()

mtor/parameters.py

@@ -8,7 +8,7 @@ from .commons import RE_DIGITS, OUTPUT_FOLDER_NAMES, TifImage
 class Parameters(dict):
     """ namespacing the parameters for a analysis with nice access
 
-    the following parameters are used:
+    parmeters used for analysing the images
 
     boost:                  linear factor for brightness on color coded images
     charts_y_limit:         common scale for the y axis in graphs
@@ -42,7 +42,7 @@ class Parameters(dict):
     right_guard_column:     name of the most used left guard value column
     right_guard_name:       right guard value group name
     roi_bottom:             bottom edge of the ROI
-    roi_column:
+    roi_column:             name for the ROI
     roi_left:               bottom left of the ROI
     roi_name:               roi value group name
     roi_right:              bottom right of the ROI
@@ -177,3 +177,12 @@ class Parameters(dict):
         self.cut_bottom = max(0, bottom + self.cut_pad_y)
         self.cut_right = min(self.image_width, right + self.cut_pad_x)
         self.cut_left = max(0, left - self.cut_pad_x)
+
+    def get_descriptions(self):
+        docs = type(self).__doc__
+        lines = (line.strip() for line in docs.split("\n"))
+        has_colon = (line for line in lines if ":" in line)
+        pairs = (line.split(":") for line in has_colon)
+        pairs_cleanup = ((k.strip(), v.strip()) for k, v in pairs)
+        non_empty = ((k, v) for k, v in pairs_cleanup if k and v)
+        return dict(non_empty)