Merge branch 'development' into feature/thermal_cx_line

.github/workflows/ci.yml

@@ -11,7 +11,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        numpy-version: ["oldest-supported-numpy", "numpy"]
+        numpy-version: ["oldest-supported-numpy", "'numpy<2'"]
         python-version: ["3.7", "3.8", "3.9", "3.10"]
     steps:
     - name: Checkout code

CHANGELOG.md

@@ -15,6 +15,7 @@ New:
 * Improved parsing of metadata from the ADAS ADF15 'bnd' files for H-like ions. Raises a runtime error if the metadata cannot be parsed. (#424)
 * **Beam dispersion calculation has changed from sigma(z) = sigma + z * tan(alpha) to sigma(z) = sqrt(sigma^2 + (z * tan(alpha))^2) for consistancy with the Gaussian beam model. Attention!!! The results of BES and CX spectroscopy are affected by this change. (#414)**
 * Improved beam direction calculation to allow for natural broadening of the BES line shape due to beam divergence. (#414)
+* Add kwargs to invert_regularised_nnls to pass them to scipy.optimize.nnls. (#438)
 * Add thermal charge-exchange emission model. (#57)
 * PECs for C VI spectral lines for n <= 5 are now included in populate(). Rerun populate() after upgrading to 1.5 to update the atomic data repository.
 

cherab/tools/inversions/nnls.py

@@ -21,21 +21,25 @@
 import scipy
 
 
-def invert_regularised_nnls(w_matrix, b_vector, alpha=0.01, tikhonov_matrix=None):
-    """
+def invert_regularised_nnls(w_matrix, b_vector, alpha=0.01, tikhonov_matrix=None, **kwargs):
+    r"""
     Solves :math:`\mathbf{b} = \mathbf{W} \mathbf{x}` for the vector :math:`\mathbf{x}`,
     using Tikhonov regulariastion.
 
     This is a thin wrapper around scipy.optimize.nnls, which modifies
     the arguments to include the supplied Tikhonov regularisation matrix.
 
+    The values of w_matrix, b_vector and alpha * tikhonov_matrix are notmalised
+    by max(b_vector) before passing them to scipy.optimize.nnls().
+
     :param np.ndarray w_matrix: The sensitivity matrix describing the coupling between the
       detectors and the voxels. Must be an array with shape :math:`(N_d, N_s)`.
     :param np.ndarray b_vector: The measured power/radiance vector with shape :math:`(N_d)`.
     :param float alpha: The regularisation hyperparameter :math:`\alpha` which determines
       the regularisation strength of the tikhonov matrix.
     :param np.ndarray tikhonov_matrix: The tikhonov regularisation matrix operator, an array
       with shape :math:`(N_s, N_s)`. If None, the identity matrix is used.
+    :param **kwargs: Keyword arguments passed to scipy.optimize.nnls.
     :return: (x, norm), the solution vector and the residual norm.
 
     .. code-block:: pycon
@@ -60,6 +64,9 @@ def invert_regularised_nnls(w_matrix, b_vector, alpha=0.01, tikhonov_matrix=None
     d_vector = np.zeros(m+n)
     d_vector[0:m] = b_vector[:]
 
-    x_vector, rnorm = scipy.optimize.nnls(c_matrix, d_vector)
+    # Normalise c_matrix and d_vector to avoid possible issues with the nnls termination criteria.
+    vmax = d_vector.max()
+
+    x_vector, rnorm = scipy.optimize.nnls(c_matrix / vmax, d_vector / vmax, **kwargs)
 
-    return x_vector, rnorm
+    return x_vector, rnorm * vmax

requirements.txt

@@ -1,5 +1,5 @@
 cython>=0.28
-numpy>=1.14
+numpy>=1.14,<2.0
 scipy
 matplotlib
 raysect==0.8.1

setup.py

@@ -115,7 +115,7 @@
     long_description=long_description,
     long_description_content_type="text/markdown",
     install_requires=[
-        "numpy>=1.14",
+        "numpy>=1.14,<2.0",
         "scipy",
         "matplotlib",
         "raysect==0.8.1",