fix: Remove API for calculating mu from packing fraction

news/rm-packing-fraction.rst

@@ -0,0 +1,23 @@
+**Added:**
+
+* <news item>
+
+**Changed:**
+
+* <news item>
+
+**Deprecated:**
+
+* <news item>
+
+**Removed:**
+
+* Removed API that calculates mu from packing fraction.
+
+**Fixed:**
+
+* <news item>
+
+**Security:**
+
+* <news item>

src/diffpy/utils/tools.py

@@ -231,49 +231,30 @@ def get_density_from_cloud(sample_composition, mp_token=""):
     )
 
 
-def compute_mu_using_xraydb(
-    sample_composition, energy, sample_mass_density=None, packing_fraction=None
-):
+def compute_mu_using_xraydb(sample_composition, energy, sample_mass_density):
     """Compute the attenuation coefficient (mu) using the XrayDB
     database.
 
-    Computes mu based on the sample composition and energy.
-    User should provide a sample mass density or a packing fraction.
-    If neither density nor packing fraction is specified,
-    or if both are specified, a ValueError will be raised.
-    Reference: https://xraypy.github.io/XrayDB/python.html#xraydb.material_mu.
+    Computes mu based on the sample composition, X-ray energy, and
+    sample mass density.
+
+    Reference:
+    https://xraypy.github.io/XrayDB/python.html#xraydb.material_mu
 
     Parameters
     ----------
     sample_composition : str
         The chemical formula of the material.
     energy : float
-        The energy of the incident x-rays in keV.
-    sample_mass_density : float, ``optional``
-        The mass density of the packed powder/sample in g/cm*3.
-        Default is None.
-    packing_fraction : float, ``optional``
-        The fraction of sample in the capillary (between 0 and 1).
-        Specify either sample_mass_density or packing_fraction but not both.
-        Default is None.
+        The energy of the incident X-rays in keV.
+    sample_mass_density : float
+        The mass density of the sample in g/cm^3.
 
     Returns
     -------
     mu : float
-        The attenuation coefficient mu in mm^{-1}.
+        The attenuation coefficient μ in mm⁻¹.
     """
-    if (sample_mass_density is None and packing_fraction is None) or (
-        sample_mass_density is not None and packing_fraction is not None
-    ):
-        raise ValueError(
-            "You must specify either sample_mass_density or packing_fraction, "
-            "but not both. "
-            "Please rerun specifying only one."
-        )
-    if packing_fraction is not None:
-        sample_mass_density = (
-            get_density_from_cloud(sample_composition) * packing_fraction
-        )
     energy_eV = energy * 1000
     mu = (
         material_mu(

tests/test_tools.py

@@ -9,7 +9,6 @@
 from diffpy.utils.tools import (
     _extend_z_and_convolve,
     check_and_build_global_config,
-    compute_mu_using_xraydb,
     compute_mud,
     get_package_info,
     get_user_info,
@@ -270,40 +269,6 @@ def test_get_package_info(monkeypatch, inputs, expected):
     assert actual_metadata == expected
 
 
-@pytest.mark.parametrize(
-    "inputs",
-    [
-        # Test when the function has invalid inputs
-        (  # C1: Both mass density and packing fraction are provided,
-            # expect ValueError exception
-            {
-                "sample_composition": "SiO2",
-                "energy": 10,
-                "sample_mass_density": 2.65,
-                "packing_fraction": 1,
-            }
-        ),
-        (  # C2: None of mass density or packing fraction are provided,
-            # expect ValueError exception
-            {
-                "sample_composition": "SiO2",
-                "energy": 10,
-            }
-        ),
-    ],
-)
-def test_compute_mu_using_xraydb_bad(inputs):
-    with pytest.raises(
-        ValueError,
-        match=(
-            "You must specify either sample_mass_density or "
-            "packing_fraction, but not both. "
-            "Please rerun specifying only one."
-        ),
-    ):
-        compute_mu_using_xraydb(**inputs)
-
-
 def test_compute_mud(tmp_path):
     diameter, slit_width, z0, I0, mud, slope = 1, 0.1, 0, 1e5, 3, 0
     z_data = np.linspace(-1, 1, 50)