Grab reference gain/value metadata also for custom-named filtersets (#9)

* Add regression test against #8
See #9 for evidence that it works.

* Use filtername lookup to find reference gain/value entries

Closes #8

bletl/__init__.py

@@ -13,4 +13,4 @@
 from . splines import get_crossvalidated_spline
 
 
-__version__ = '1.0.3'
+__version__ = '1.0.4'

bletl/parsing/blpro.py

@@ -177,9 +177,15 @@ def extract_filtersets(metadata):
         filtersets[num][key] = v
 
     # grab data from metadata['process']
+    fname_lookup = {
+        "201": "Biomass",
+        "202": "pH(HP8)",
+        "203": "DO(PSt3)",
+    }
     for fnum, fset in filtersets.items():
         for k in ['reference_value', 'reference_gain', 'gain']:
-            pk = '{:02d}_{}_{}'.format(fnum, k, fset['name'])
+            fn = fname_lookup.get(fset['filter_id'], fset['name'])
+            pk = f'{fnum:02d}_{k}_{fn}'
             if pk in process:
                 filtersets[fnum][k] = process.pop(pk)
 
@@ -491,4 +497,4 @@ def calibrate_DO(raw, cal_0, cal_100):
     S_cal_100 = numpy.tan(cal_100 * numpy.pi / 180)
     ksv = 0.01 * ((S_cal_0 / S_cal_100) - 1)
     DO = (1 / ksv) * ((S_cal_0 / numpy.tan(raw * numpy.pi / 180)) - 1)
-    return DO
+    return DO

tests/test_core.py

@@ -416,6 +416,10 @@ def test_parse_metadata_data(self):
 
             assert isinstance(metadata, dict)
             assert isinstance(data, pandas.DataFrame)
+
+            # 👇 Regression check against https://github.com/JuBiotech/bletl/issues/8
+            filtersets = bletl.parsing.blpro.extract_filtersets(metadata)
+            assert "01_reference_gain_Biomass" not in metadata["process"]
         return
 
     def test_parsing(self):