remove custom normalization map

normilization of exposure time is always to the largest time available

CHANGES.md

@@ -1,3 +1,15 @@
+0.2.0  - remove custom normalization map
+----------------------------------------
+
+ - normilization of exposure time is always to the largest time available
+
+
+0.1.0  - first working version
+------------------------------
+
+ - woohoo
+
+
 0.0.1  - first version
 ----------------------
 

sensospot_data/__init__.py

@@ -3,7 +3,7 @@
 Parsing the numerical output from Sensovations Sensospot image analysis.
 """
 
-__version__ = "0.1.0"
+__version__ = "0.2.0"
 
 VERSION_TABLE_NAME = f"v{__version__}".replace(".", "_")
 

sensospot_data/normalisation.py

@@ -123,21 +123,14 @@ def _infer_normalization_map(split_data_frames):
     }
 
 
-def normalize_exposure_time(split_data_frames, normalization_map=None):
+def normalize_exposure_time(split_data_frames):
     """ add time normalized values to the split data frames
 
-    normalization_map:
-        keys: channel identifier (e.g. "Cy5")
-        values: target exposure time for normalization
-
-    If normalization_map is None, the max exposure time per channel is used
+    The max exposure time per channel is used for normalization.
     """
-    complete_map = _infer_normalization_map(split_data_frames)
-    if normalization_map is not None:
-        complete_map.update(normalization_map)
-
+    normalization_map = _infer_normalization_map(split_data_frames)
     return {
-        key: _normalize_exposure(frame, complete_map[key])
+        key: _normalize_exposure(frame, normalization_map[key])
         for key, frame in split_data_frames.items()
     }
 
@@ -157,7 +150,6 @@ def _normalize_exposure(channel_frame, normalized_time):
 def normalize_measurement(
     data_frame,
     exposure_map=None,
-    normalization_map=None,
     overflow_column=COL_NAME_SPOT_MEAN,
     overflow_limit=0.5,
 ):
@@ -169,14 +161,11 @@ def normalize_measurement(
     if the exposure map is None, the values from the optionally parsed
     measurement parameters are used.
 
-    normalization_map:
-        keys: channel identifier (e.g. "Cy5")
-        values: target exposure time for normalization
-    If normalization_map is None, the max exposure time per channel is used
+    The max exposure time per channel is used for normalization.
     """
 
     exposure_data_frame = apply_exposure_map(data_frame, exposure_map)
     split_data_frames = reduce_overflow(
         exposure_data_frame, overflow_column, overflow_limit
     )
-    return normalize_exposure_time(split_data_frames, normalization_map)
+    return normalize_exposure_time(split_data_frames)

tests/test_normailsation.py

@@ -228,7 +228,7 @@ def test_normalize_exposure(normalization_data_frame):
     sorted_results = result.sort_values(
         by=["Well.Row", "Well.Column", "Pos.Id"]
     )
-    expected_values = [2, 8, 30, 2, 20, 20, 20, 20, 200, 200, 200, 200]
+    expected_values = [1, 4, 15, 1, 10, 10, 10, 10, 100, 100, 100, 100]
 
     for normalized_col in COLUMN_NORMALIZATION.values():
         list(sorted_results[normalized_col]) == expected_values
@@ -241,14 +241,14 @@ def test_normalize_exposure_time(normalization_data_frame):
     )
 
     reduced = reduce_overflow(normalization_data_frame, "Saturation", 1)
-    result = normalize_exposure_time(reduced, {"Cy5": 100, "Cy3": 0})
+    result = normalize_exposure_time(reduced)
 
     assert "Cy5" in result
 
     sorted_results = result["Cy5"].sort_values(
         by=["Well.Row", "Well.Column", "Pos.Id"]
     )
-    expected_values = [2, 8, 30, 2, 20, 20, 20, 20, 200, 200, 200, 200]
+    expected_values = [1, 4, 15, 1, 10, 10, 10, 10, 100, 100, 100, 100]
 
     assert list(sorted_results["Normalized.Spot.Mean"]) == expected_values
 
@@ -284,11 +284,10 @@ def test_normalize_measurement(example_dir):
         2: ExposureSetting("Cy5", 15),
         3: ExposureSetting("Cy5", 150),
     }
-    normalization_map = {"Cy5": 25}
 
-    result = normalize_measurement(data_frame, exposure_map, normalization_map)
+    result = normalize_measurement(data_frame, exposure_map)
     cy3_df, cy5_df = result["Cy3"], result["Cy5"]
 
     assert set(result.keys()) == {"Cy3", "Cy5"}
     assert cy3_df["Normalized.Exposure.Time"].unique() == 100
-    assert cy5_df["Normalized.Exposure.Time"].unique() == 25
+    assert cy5_df["Normalized.Exposure.Time"].unique() == 150