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23 changes: 23 additions & 0 deletions news/mu.rst
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@@ -0,0 +1,23 @@
**Added:**

* function to compute x-ray attenuation coefficient (mu) using XrayDB

**Changed:**

* <news item>

**Deprecated:**

* <news item>

**Removed:**

* <news item>

**Fixed:**

* <news item>

**Security:**

* <news item>
1 change: 1 addition & 0 deletions requirements/conda.txt
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@@ -1 +1,2 @@
numpy
xraydb
1 change: 1 addition & 0 deletions requirements/pip.txt
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@@ -1 +1,2 @@
numpy
xraydb
31 changes: 31 additions & 0 deletions src/diffpy/utils/tools.py
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Expand Up @@ -3,6 +3,8 @@
from copy import copy
from pathlib import Path

from xraydb import material_mu


def _stringify(obj):
"""
Expand Down Expand Up @@ -131,3 +133,32 @@ def get_package_info(package_names, metadata=None):
pkg_info.update({package: importlib.metadata.version(package)})
metadata["package_info"] = pkg_info
return metadata


def compute_mu_using_xraydb(sample_composition, energy, density=None, packing_fraction=1):
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added packing fraction. does this make sense or should we use None instead? I think if we set the default to 1 the code can be more concise

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I think setting to None is clearer here. The logic from the perspective of the user is that we either have a packing fraction or we don't. If it is None, you can set it to 1 in the code.

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Also, didn't we discuss to change the name to sample_mass_density?

"""Compute the attenuation coefficient (mu) using the XrayDB database.

Computes mu based on the sample composition and energy.
If density is not provided, a standard reference density (e.g., 0.987 g/cm^3 for H2O) is used.
User can provide either a measured density or an estimated packing fraction (with a standard density).
It is recommended to specify the density, especially for materials like ZrO2, where it can vary.
Reference: https://xraypy.github.io/XrayDB/python.html#xraydb.material_mu.

Parameters
----------
sample_composition : str
The chemical formula or the name of the material.
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not the name, just chemical formula.

energy : float
The energy in eV.
density : float, optional, Default is None
The mass density of the packed powder/sample in gr/cm^3. If None, a standard density from XrayDB is used.
packing_fraction : float, optional, Default is 1
The fraction of sample in the capillary (between 0 and 1).

Returns
-------
mu : float
The attenuation coefficient mu in mm^{-1}.
"""
mu = material_mu(sample_composition, energy, density=density, kind="total") * packing_fraction / 10
return mu
64 changes: 63 additions & 1 deletion tests/test_tools.py
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@

import pytest

from diffpy.utils.tools import get_package_info, get_user_info
from diffpy.utils.tools import compute_mu_using_xraydb, get_package_info, get_user_info

# def _setup_dirs(monkeypatch, user_filesystem):
# home_dir, cwd_dir = user_filesystem.home_dir, user_filesystem.cwd_dir
Expand Down Expand Up @@ -189,3 +189,65 @@ def test_get_package_info(monkeypatch, inputs, expected):
)
actual_metadata = get_package_info(inputs[0], metadata=inputs[1])
assert actual_metadata == expected


@pytest.mark.parametrize(
"inputs, expected_mu",
[
# Test whether the function returns the correct mu
( # C1: No density or packing fraction provided, expect to compute mu based on standard density
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do we want to allow this? what is "standard density"?

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Just density I think - density under the standard conditions. So user can specify either a measured density or packing fraction.

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The reason I'm not putting a strict "and/or" for density and packing fraction is that xraydb does not know the density to some materials like ZrO2. In this case user needs to put a density and a packing fraction, or they can put their measured density.

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I see, so density is in the database? that is unexpected.

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how does xraydb compute the density?

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I see, it has a database of some standard materials. So we will need tests for cases where the material can be found and cases where it can't. How many different materials are in that database? The examples show just a few satandard materials (Kapton, quartz, etc.). Is it an extensive database or just a few x-ray standards?

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How will you compute it for the cases where it is not in the db?

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They have densities for some materials (for example, this returns sample name and density: https://xraypy.github.io/XrayDB/python.html#xraydb.get_materials).
I just reread their docstring and their density refers to the material density, not mass density: https://xraypy.github.io/XrayDB/python.html#xraydb.material_mu.

{
"sample_composition": "H2O",
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water is probably not a good "illustrative" choice. Maybe use quartz instead?

"energy": 10000,
"density": None,
"packing_fraction": 1,
},
0.5330,
),
( # C2: Packing fraction (=0.5) provided only, expect to return half of mu based on standard density
{
"sample_composition": "H2O",
"energy": 10000,
"density": None,
"packing_fraction": 0.5,
},
0.2665,
),
( # C3: Density provided only, expect to compute mu based on density
# 1. Standard density provided, expect to return the same mu as C1
{
"sample_composition": "H2O",
"energy": 10000,
"density": 0.997,
"packing_fraction": 1,
},
0.5330,
),
( # 2. Lower density for H2O (half of standard), expect to return half of mu based on standard density
{
"sample_composition": "H2O",
"energy": 10000,
"density": 0.4985,
"packing_fraction": 1,
},
0.2665,
),
( # C4: Both standard density and packing fraction are provided, expect to compute the same mu as C2
{
"sample_composition": "H2O",
"energy": 10000,
"density": 0.997,
"packing_fraction": 0.5,
},
0.2665,
),
],
)
def test_compute_mu_using_xraydb(inputs, expected_mu):
actual_mu = compute_mu_using_xraydb(
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here you put

def test_compute_mu_using_xraydb(inputs, expected_mu):
    actual_mu = compute_mu_using_xraydb(**inputs)

which will unpack the input dict. You may have to handle the required args differently, but you can then have input_args and input_kwargs in your paramatrize and unpack them differently.

inputs["sample_composition"],
inputs["energy"],
density=inputs["density"],
packing_fraction=inputs["packing_fraction"],
)
assert actual_mu == pytest.approx(expected_mu, rel=0.01, abs=0.1)
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this seems a bit too loose?

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