Several improvements to capabilities and build.

- cmake fixed, no longer needs numpy headers.
- models can be loaded from an external interepreter.

examples/mliappy/README

@@ -0,0 +1,26 @@
+README for MLIAPPY Example
+
+These examples run the Ta06 example from the MLIAP package, but using the python coupling.
+
+1: Running models using LAMMPS executable: in.mliap.snap.Ta06A.
+
+To run this, first run convert_mliap_Ta06A.py, which will convert the Ta06 potential into a pytorch model.
+It will be saved as "Ta06A.mliap.pytorch.model.pkl".
+
+It will also copy the "mliappy_pytorch.py" file into the current working directory. mliappy_pytorch.py contains
+class definitions suitable for wrapping an arbitrary energy model MLIAPPY. It must be available to python when
+creating or unpicking a pytorch model for MLIAPPY.
+
+From that point you can run the example lmp -in in.mliap.snap.Ta06A -echo both
+
+2: Running models from python with LAMMPS in library mode: load_external.py
+
+Before testing this, ensure that the first example (using LAMMPS executable) works.
+
+Not all python installations support this mode of operation. 
+Too test if your interpreter supports this, run:
+`python test_pylibs.py`
+ and examine the output.
+
+If this succeeds, you should be able to run:
+`python load_external.py`

examples/mliappy/README.txt

@@ -1,11 +0,0 @@
-README for MLIAPPY Example
-
-This example runs the Ta06 example from the MLIAP example, but using the python coupling.
-
-To run this, first run convert_mliap_Ta06A.py, which will convert the Ta06 potential into a pytorch model.
-It will be saved as "Ta06A.mliap.pytorch.model.pkl".
-
-It will also copy the "torchlink.py" file into the current working directory. torchlink.py contains
-class definitions suitable for wrapping an arbitrary energy model MLIAPPY.
-
-From that point you can run the example lmp -in in.mliap.snap.Ta06A -echo both

examples/mliappy/convert_mliap_Ta06A.py

@@ -22,7 +22,7 @@ with torch.autograd.no_grad():
     lin.bias.set_(torch.as_tensor(bias,dtype=torch.float64).unsqueeze(0))
 
 # Wrap the pytorch model for usage with MLIAPPY
-model = mliappy_pytorch.IgnoreTypes(lin)
+model = mliappy_pytorch.IgnoreElems(lin)
 n_descriptors = lin.weight.shape[1]
 n_params = mliappy_pytorch.calc_n_params(model)
 n_types = 1
@@ -30,4 +30,4 @@ linked_model = mliappy_pytorch.TorchWrapper64(model,n_descriptors=n_descriptors,
 
 # Save the result.
 with open("Ta06A.mliap.pytorch.model.pkl",'wb') as pfile:
-    pickle.dump(linked_model,pfile)
+    pickle.dump(linked_model,pfile)

examples/mliappy/in.mliap.pytorch.Ta06A

@@ -1,4 +1,4 @@
-# Demonstrate MLIAP interface to kinear SNAP potential
+# Demonstrate MLIAP interface to linear SNAP potential
 
 # Initialize simulation
 

examples/mliappy/load_external.py

@@ -0,0 +1,104 @@
+
+# Demonstrate how to load a model from the python side.
+# This is essentially the same as in.mliap.pytorch.Ta06A--
+# except that python is the driving program, and lammps
+# is in library mode.
+
+before_loading =\
+"""
+  # Demonstrate MLIAP interface to linear SNAP potential
+
+  # Initialize simulation
+
+  variable nsteps index 100
+  variable nrep equal 4
+  variable a equal 3.316
+  units           metal
+
+  # generate the box and atom positions using a BCC lattice
+
+  variable nx equal ${nrep}
+  variable ny equal ${nrep}
+  variable nz equal ${nrep}
+
+  boundary        p p p
+
+  lattice         bcc $a
+  region          box block 0 ${nx} 0 ${ny} 0 ${nz}
+  create_box      1 box
+  create_atoms    1 box
+
+  mass 1 180.88
+  
+  # choose potential
+  # DATE: 2014-09-05 UNITS: metal CONTRIBUTOR: Aidan Thompson athomps@sandia.gov CITATION: Thompson, Swiler, Trott, Foiles and Tucker, arxiv.org, 1409.3880 (2014)
+    
+  # Definition of SNAP potential Ta_Cand06A
+  # Assumes 1 LAMMPS atom type
+
+  variable zblcutinner equal 4
+  variable zblcutouter equal 4.8
+  variable zblz equal 73
+
+  # Specify hybrid with SNAP, ZBL
+
+  pair_style hybrid/overlay &
+  zbl ${zblcutinner} ${zblcutouter} &
+  mliap model mliappy LATER &
+  descriptor sna Ta06A.mliap.descriptor
+  pair_coeff 1 1 zbl ${zblz} ${zblz}
+  pair_coeff * * mliap Ta
+"""
+after_loading =\
+"""
+
+  # Setup output
+
+  compute  eatom all pe/atom
+  compute  energy all reduce sum c_eatom
+
+  compute  satom all stress/atom NULL
+  compute  str all reduce sum c_satom[1] c_satom[2] c_satom[3]
+  variable press equal (c_str[1]+c_str[2]+c_str[3])/(3*vol)
+
+  thermo_style    custom step temp epair c_energy etotal press v_press
+  thermo          10
+  thermo_modify norm yes
+
+  # Set up NVE run
+
+  timestep 0.5e-3
+  neighbor 1.0 bin
+  neigh_modify once no every 1 delay 0 check yes
+
+  # Run MD
+
+  velocity all create 300.0 4928459 loop geom
+  fix 1 all nve
+  run             ${nsteps}
+"""
+
+
+import lammps
+
+lmp = lammps.lammps(cmdargs=['-echo','both'])
+# This commmand must be run before the MLIAP object is declared in lammps.
+lmp.mliappy.activate()
+
+lmp.commands_string(before_loading)
+
+# Now the model is declared, but empty -- because the model filename
+# was given as "LATER".
+
+# Load the python module, construct on the fly, do whatever, here:
+import pickle
+with open('Ta06A.mliap.pytorch.model.pkl','rb') as pfile:
+  model = pickle.load(pfile)
+  
+# Now that you have a model, connect it to the pairstyle
+lmp.mliappy.load_model(model)
+
+# Proceed with whatever calculations you like.
+lmp.commands_string(after_loading)
+
+

examples/mliappy/test_pylibs.py

@@ -0,0 +1,12 @@
+import sysconfig, os,ctypes
+
+library = sysconfig.get_config_vars('INSTSONAME')[0]
+
+pylib = ctypes.CDLL(library)
+
+connected = pylib.Py_IsInitialized()
+
+if not connected:
+      print("FAILURE: This interpreter is not compatible with python-driven mliappy.")
+else:
+      print("SUCCESS: This interpreter is compatible with python-driven MLIAPPY")