Merge pull request #44 from BAMresearch/fix_unit_tests

Fix unit tests

Author: toqduj

src/modacor/tests/test_basedata.py

@@ -8,11 +8,6 @@
 from ..dataclasses.basedata import BaseData  # adjust the import path as needed
 
 
-# Create a dummy client to use as the data_source
-class DummyClient:
-    pass
-
-
 @pytest.fixture
 def sample_data():
     return np.arange(5)
@@ -21,29 +16,19 @@ def sample_data():
 def test_data_and_display_data_properties(sample_data):
     # Create an instance of BaseData with test values
     bd = BaseData(
-        ingest_units=ureg.m,  # meters
-        internal_units=ureg.m,  # meters
-        display_units=ureg.cm,  # centimeters
-        data_source=DummyClient(),
-        raw_data=sample_data,
+        signal_units=ureg.m,  # meters
+        signal=sample_data,
         normalization=np.ones_like(sample_data) * 2,
         # uncertainties=[],            # empty list for uncertainties
         # scalar=2.0,
         # scalar_uncertainty=0.1,
-        provenance=[],  # empty provenance
         rank_of_data=1,  # valid since sample_data.ndim is 1
     )
 
     # Test the 'data' property
-    expected_data = sample_data * 2.0
+    expected_data = sample_data / 2.0
     # Here we check that the computed values match the expected ones.
-    assert (bd.data == expected_data).all(), "data property did not apply the scalar correctly."
-
-    # Test the 'display_data' property
-    # The conversion from meters to centimeters yields a factor of 100.
-    conversion_factor = (1 * ureg.Unit("m")).to("cm").magnitude  # should be 100.0
-    expected_display = sample_data * 2.0 * conversion_factor
-    assert (bd.display_data == expected_display).all(), "display_data property did not convert units correctly."
+    assert (bd.mean() == expected_data).all(), "data property did not apply the scalar correctly."
 
 
 def test_rank_validation_exceeds_ndim():
@@ -53,15 +38,11 @@ def test_rank_validation_exceeds_ndim():
     # Attempting to set rank_of_data=3 (while arr.ndim is 2) should raise a ValueError.
     with pytest.raises(ValueError) as exc_info:
         BaseData(
-            ingest_units=ureg.m,
-            internal_units=ureg.m,
-            display_units=ureg.cm,
-            data_source=DummyClient(),
-            raw_data=arr,
+            signal_units=ureg.m,
+            signal=arr,
             # uncertainties=[],
             # scalar=1.0,
             # scalar_uncertainty=0.0,
-            provenance=[],
             rank_of_data=3,  # invalid, since 3 > arr.ndim (2)
         )
-    assert "cannot exceed the dimensionality of internal_data" in str(exc_info.value)
+    assert "cannot exceed the dimensionality of signal" in str(exc_info.value)

src/modacor/tests/test_variance_calculations.py

@@ -9,6 +9,7 @@
 import numpy as np
 
 from ..math import variance_calculations as varc
+from uncertainties import unumpy
 
 samples = 1000
 
@@ -30,14 +31,14 @@ def test_add():
     y = generate_samples(samples)
     dy = generate_error(y, add_zero_errors=True)
 
-    u_x = uarray(x, dx)
-    u_y = uarray(y, dy)
+    u_x = unumpy.uarray(x, dx)
+    u_y = unumpy.uarray(y, dy)
     expected = u_x + u_y
 
     result, variances = varc.add(x, dx**2, y, dy**2)
 
-    assert np.allclose(result, nominal_values(expected))
-    assert np.allclose(variances, std_devs(expected) ** 2)
+    assert np.allclose(result, unumpy.nominal_values(expected))
+    assert np.allclose(variances, unumpy.std_devs(expected) ** 2)
 
 
 def test_subtract():
@@ -46,14 +47,14 @@ def test_subtract():
     y = generate_samples(samples)
     dy = generate_error(y, add_zero_errors=True)
 
-    u_x = uarray(x, dx)
-    u_y = uarray(y, dy)
+    u_x = unumpy.uarray(x, dx)
+    u_y = unumpy.uarray(y, dy)
     expected = u_x - u_y
 
     result, variances = varc.subtract(x, dx**2, y, dy**2)
 
-    assert np.allclose(result, nominal_values(expected))
-    assert np.allclose(variances, std_devs(expected) ** 2)
+    assert np.allclose(result, unumpy.nominal_values(expected))
+    assert np.allclose(variances, unumpy.std_devs(expected) ** 2)
 
 
 def test_multiply():
@@ -62,14 +63,14 @@ def test_multiply():
     y = generate_samples(samples)
     dy = generate_error(y, add_zero_errors=True)
 
-    u_x = uarray(x, dx)
-    u_y = uarray(y, dy)
+    u_x = unumpy.uarray(x, dx)
+    u_y = unumpy.uarray(y, dy)
     expected = u_x * u_y
 
     result, variances = varc.multiply(x, dx**2, y, dy**2)
 
-    assert np.allclose(result, nominal_values(expected))
-    assert np.allclose(variances, std_devs(expected) ** 2)
+    assert np.allclose(result, unumpy.nominal_values(expected))
+    assert np.allclose(variances, unumpy.std_devs(expected) ** 2)
 
 
 def test_divide():
@@ -78,11 +79,11 @@ def test_divide():
     y = generate_samples(samples, low=1e3)  # avoid divide by very small
     dy = generate_error(y, add_zero_errors=True)
 
-    u_x = uarray(x, dx)
-    u_y = uarray(y, dy)
+    u_x = unumpy.uarray(x, dx)
+    u_y = unumpy.uarray(y, dy)
     expected = u_x / u_y
 
     result, variances = varc.divide(x, dx**2, y, dy**2)
 
-    assert np.allclose(result, nominal_values(expected))
-    assert np.allclose(variances, std_devs(expected) ** 2)
+    assert np.allclose(result, unumpy.nominal_values(expected))
+    assert np.allclose(variances, unumpy.std_devs(expected) ** 2)