corrected / detailed in-code doku of anomaly fixtures (realized to forget them briefly)

test/funcs/conftest.py

@@ -12,6 +12,7 @@ def char_dict():
 
 @pytest.fixture
 def course_1(char_dict):
+    # MONOTONOUSLY ASCENDING/DESCENDING
     # values , that monotonously ascend towards a peak level, and thereafter do monotonously decrease
     # the resulting drop/raise per value equals:  (peak_level - initial_level) / (0.5*(periods-2))
     # periods number better be even!
@@ -32,6 +33,7 @@ def course_1(char_dict):
 
 @pytest.fixture
 def course_2(char_dict):
+    # SINGLE_SPIKE
     # values , that linearly  develop over the whole timeseries, from "initial_level" to "final_level", exhibiting
     # one "anomalous" or "outlierish" value of magnitude "out_val" at position "periods/2"
     # number of periods better be even!
@@ -59,9 +61,13 @@ def course_2(char_dict):
 
 @pytest.fixture
 def course_3(char_dict):
+    # CROWD IN A PIT/CROWD ON A SUMMIT
     # values , that linearly  develop over the whole timeseries, from "initial_level" to "final_level", exhibiting
-    # one "anomalous" or "outlierish" value of magnitude "out_val" at position "periods/2"
+    # a "crowd" of "anomalous" or "outlierish" values of magnitude "out_val".
+    # The "crowd/group" of anomalous values starts at position "periods/2" and continues with an additional amount
+    # of "crowd_size" values, that are each spaced "crowd_spacing" minutes from there predecessors.
     # number of periods better be even!
+    # chrowd_size * crowd_spacing better be less then freq[minutes].
     def fix_funk(freq='10min', periods=10, initial_level=0, final_level=2, out_val=-5,
                  initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0), char_dict=char_dict, crowd_size=5, crowd_spacing=1):
 
@@ -87,8 +93,9 @@ def course_3(char_dict):
 
 @pytest.fixture
 def course_4(char_dict):
-    # values , that linearly  develop over the whole timeseries, from "initial_level" to "final_level", exhibiting
-    # one "anomalous" or "outlierish" value of magnitude "out_val" at position "periods/2"
+    # TEETH (ROW OF SPIKES)
+    # values , that remain on value level "base_level" and than begin exposing an outlierish or spikey value of magnitude
+    # "out_val" every second timestep, starting at periods/2, with the first spike.
     # number of periods better be even!
     def fix_funk(freq='10min', periods=10, base_level=0, out_val=5,
                  initial_index=pd.Timestamp(2000, 1, 1, 0, 0, 0), char_dict=char_dict):