Merge branch 'master' into alm-add-vector-dot

README.md

 # GALUMPH #
 
-Calculate ALM (scattering amplitude decomposed into spherical harmonics) using GPU acceleration.
+Calculate ALM (scattering amplitude decomposed into spherical harmonics) at Hyperspeed using GPU acceleration.
 
 This is a preliminary implementation using PyOpenCL.
+
+Example code:
+```python
+import numpy as np
+import Bio.PDB
+import periodictable
+import galumph
+
+
+NS = 4096   # Number of S values at which to calculate the scattering
+smax = 1.0  # Maximum S value
+LMAX = 63   # Maximum harmonic order to use for the calculations
+
+## Initialise the S array and allocate the ALM storage on the GPU
+s = np.linspace(0, smax, NS)
+atomalm = galumph.AtomAlm(LMAX, s)
+
+## Use Bio.PDB to read the structure and periodictable to calculate the atomic form factors
+pdb = Bio.PDB.PDBParser().get_structure("6lyz", "6lyz.pdb")
+xyz = np.array([aa.get_vector().get_array() for aa in pdb.get_atoms()])
+ff = np.array([periodictable.elements.symbol(aa.element).xray.f0(s) for aa in pdb.get_atoms()])
+
+## Run the GPU calculation
+atomalm.add_many_atoms(xyz, ff)
+Icalc = atomalm.sum_intensity()
+```
\ No newline at end of file

galumph/atomalm.py

@@ -56,7 +56,8 @@ class AtomAlm:
     def __init__(self, LMAX, s, worksize=64, natwork=16):
         assert LMAX >= 0
         self.LMAX = LMAX
-        NS, = np.shape(s)
+        s = np.asanyarray(s)
+        NS, = s.shape
         assert NS > 0
         assert np.all(s >= 0)
         self.NS = NS
@@ -86,6 +87,7 @@ class AtomAlm:
         NS = self.NS
         worksize = self.worksize
 
+        ff = np.asanyarray(ff)
         if np.isscalar(ff):
             ff = np.ones_like(self.s) * ff
         else:
@@ -119,6 +121,7 @@ class AtomAlm:
         assert natoms > 0
         assert ndim == 3
 
+        ff = np.asanyarray(ff)
         if np.ndim(ff) == 1:
             ff = np.ones_like(self.s) * ff[:, np.newaxis]
         assert np.shape(ff) == (natoms, NS)

galumph/sphericalbessel.py

@@ -26,6 +26,7 @@ class SphericalBessel:
         the worksize parameter. LMAX should be a positive integer."""
         assert LMAX >= 0
         self.LMAX = LMAX
+        s = np.asanyarray(s)
         NS, = s.shape
         assert NS > 0
         assert np.all(s >= 0)