refactor tests

tests/test_calcium.py

@@ -1,5 +1,5 @@
-from neuroglia.calcium import MedianFilterDetrend, SavGolFilterDetrend
-from neuroglia.calcium import OASISInferer
+from neuroglia.calcium import MedianFilterDetrender, SavGolFilterDetrender
+from neuroglia.calcium import CalciumDeconvolver
 from oasis.functions import gen_data
 from sklearn.base import clone
 
@@ -10,13 +10,6 @@
 import numpy.testing as npt
 import xarray.testing as xrt
 
-# Test for proper parameter structure
-def test_params():
- fn_list = [MedianFilterDetrend(), SavGolFilterDetrend(), OASISInferer()]
- for fn in fn_list:
- new_object_params = fn.get_params(deep=False)
- for name, param in new_object_params.items():
- new_object_params[name] = clone(param, safe=False)
 
 # Test functions perform as expected
 true_b = 2
@@ -26,28 +19,33 @@ def test_params():
 LBL = ['a', 'b', 'c']
 sin_scale = 5
 
-data = y + sin_scale*np.sin(.05*TIME)[:,None]
-DFF = pd.DataFrame(data, TIME, LBL)
+# data = y
+DFF = pd.DataFrame(y, TIME, LBL)
+DFF_WITH_DRIFT = DFF.apply(lambda y: y + sin_scale*np.sin(.05*TIME),axis=0)
 
 assert np.all(np.mean(DFF) > 2)
 
-def test_MedianFilterDetrend():
- tmp = MedianFilterDetrend().fit_transform(DFF)
+def test_MedianFilterDetrender():
+ detrender = MedianFilterDetrender()
+ tmp = detrender.fit_transform(DFF_WITH_DRIFT)
  assert np.all(np.isclose(np.mean(tmp), 0, atol=.1))
+ clone(detrender)
 
-def test_SavGolFilterDetrend():
- tmp = SavGolFilterDetrend().fit_transform(DFF)
+def test_SavGolFilterDetrender():
+ detrender = SavGolFilterDetrender()
+ tmp = detrender.fit_transform(DFF_WITH_DRIFT)
  assert np.all(np.isclose(np.mean(tmp), 0, atol=.1))
+ clone(detrender)
 
-def test_OASISInferer():
- tmp = OASISInferer().fit_transform(SavGolFilterDetrend().fit_transform(DFF))
- assert np.all(np.array([np.corrcoef(true_s[n], np.array(tmp[a]))[0][1] for n,a in zip(range(3), LBL)]) > 0.6)
- tmp = OASISInferer().fit_transform(MedianFilterDetrend().fit_transform(DFF))
+def test_CalciumDeconvolver():
+ deconvolver = CalciumDeconvolver()
+ tmp = deconvolver.fit_transform(DFF)
  assert np.all(np.array([np.corrcoef(true_s[n], np.array(tmp[a]))[0][1] for n,a in zip(range(3), LBL)]) > 0.6)
+ clone(deconvolver)
 
 
 if __name__ == '__main__':
- test_MedianFilterDetrend()
- test_SavGolFilterDetrend()
- test_OASISInferer()
- test_params()
+ test_MedianFilterDetrender()
+ test_SavGolFilterDetrender()
+ test_CalciumDeconvolver()
+ # test_params()