Merge branch 'psi-rking:master' into custom_coords

README.rst

@@ -73,7 +73,9 @@ Running through QCEngine
 
 A basic driver has been implemented in QCEngine. QCEngine is built upon QCElemental which provides input
 validation and standardized input/output. To see the requirements for an Optimziation Input check MolSSI's
-`qcelemental documentation <http://docs.qcarchive.molssi.org/projects/QCElemental/en/stable/api/qcelemental.models.OptimizationInput.html#qcelemental.models.OptimizationInput>`_. **NOTE** QCElemental assumes atomic units by default::
+`qcelemental documentation <http://docs.qcarchive.molssi.org/projects/QCElemental/en/stable/api/qcelemental.models.OptimizationInput.html#qcelemental.models.OptimizationInput>`_. **NOTE** QCElemental assumes atomic units by default:
+
+.. code-block:: python
 
     import qcengine as qcng
 
@@ -108,7 +110,9 @@ validation and standardized input/output. To see the requirements for an Optimzi
     result = qcng.compute_procedure(opt_input, "optking")
 
 An explicit example of creating and running an OptimizationInput. **NOTE** Molecule.from_data seems to be the only
-place Angstroms are expected::
+place Angstroms are expected:
+
+.. code-block:: python
 
     import qcengine as qcng
 
@@ -183,7 +187,9 @@ as attributes of the OptHelper instance.
 ``CustomHelper`` accepts QCElemental and Psi4 molecules while requiring user provided gradients, energies, and possibly hessians. This may
 be useful for implementing a custom optimization driver or procedure using optking.
 
-EngineHelper::
+EngineHelper:
+
+.. code-block:: python
 
     import qcengine as qcng
 
@@ -232,7 +238,9 @@ EngineHelper::
     E = json_output["energies"][-1]
 
 `CustomHelper` can take `psi4` or `qcelemental` molecules. A simple example of a custom optimization loop is
-shown where the gradients are provided from a simple lennard jones potential::
+shown where the gradients are provided from a simple lennard jones potential:
+
+.. code-block:: python
 
     h2o = psi4.geometry(
     """ 

docs/source/coords.rst

@@ -33,7 +33,9 @@ Parentheses can be added by the user for clarity and are avoided.
 The string parsing is more robust and forgiving than in the c++ version and multiline strings and strange spacing
 is allowed.
 
-For hydrogen peroxide the following coordinates could be frozen::
+For hydrogen peroxide the following coordinates could be frozen:
+
+.. code-block:: python
 
     hooh = psi4.geometry(
         """
@@ -56,7 +58,9 @@ freezing torsions::
 
     "frozen_dihedral": "1 2 3 4"
 
-The following strings are all acceptable to freeze the cartesian coordinates of the hydrogens::
+The following strings are all acceptable to freeze the cartesian coordinates of the hydrogens:
+
+.. code-block:: python
 
     """ 1 Xyz 4 xYz """
     """ 2 xyz 3 xyz """
@@ -72,13 +76,17 @@ Ranged Coordinates
 ~~~~~~~~~~~~~~~~~~
 
 The `ranged_<coord>` follows the basic format. `1 2 ... min max`. Multiple coordinates can be assigned
-ranges by specifying them sequentially in the string. Using the above example for `HOOH`::
-    
+ranges by specifying them sequentially in the string. Using the above example for `HOOH`:
+
+.. code-block:: python
+
     params = {
         "ranged_distance": "2 3 1.38 1.42"
     }
 
-or using parentheses for clarity::
+or using parentheses for clarity:
+
+.. code-block:: python
 
     params = {
         "ranged_bend": "(1 2 3 99.0 110.0) (2 3 4 99.0 110.0)"
@@ -115,7 +123,7 @@ The important keys are
       third hydrogen atom, and the center of the two hydrogens.
       Lists of multiple indices denote the mid-point between the specified atoms.
 
-::
+.. code-block:: python
 
     h2oA = psi4.geometry(
         """

docs/source/molecule.rst

@@ -6,22 +6,28 @@ intermolecular fragments `dimerfrag.DimerFrag`. The fragments in turn are lists
 along with a numpy array for the cartesian geometry.
 NIST values for masses, and atomic numbers can be easily retrieved through `qcelemental` e.g. `qcelemental.periodictable.to_Z('O')`
 
-A fragment for water::
-    
+A fragment for water:
+
+.. code-block:: python-console
+
     >>> import optking
     >>> zs = [1, 8, 1]
     >>> masses = [1.007825032230, 15.994914619570, 1.007825032230]
-    >>> geometry = [-0.028413670411,     0.928922556351,     0.000000000000],
-                   [-0.053670056908,    -0.039737675589,     0.000000000000],
-                   [ 0.880196420813,    -0.298256807934,     0.000000000000]]
+    >>> geometry = [[-0.028413670411,     0.928922556351,     0.000000000000],
+                    [-0.053670056908,    -0.039737675589,     0.000000000000],
+                    [ 0.880196420813,    -0.298256807934,     0.000000000000]]
     >>> fragment = optking.Frag(zs, geometry, masses)
 
-The optimization coordinates can be added manually.::
+The optimization coordinates can be added manually.
+
+.. code-block:: python-console
 
     >>> intcos = [optking.Stre(1, 2), optking.Stre(2, 3), optking.Bend(1, 2, 3)]
     >>> fragment.intcos = intcos  # intcos can also be added at instantiation
 
-More typically, the coordinate system is automatically generated once the full molecular system is built and then edited if nessecary.::
+More typically, the coordinate system is automatically generated once the full molecular system is built and then edited if nessecary.
+
+.. code-block:: python-console
 
     >>> molsys = optking.Molsys([fragment])
     >>> optking.make_internal_coords(molsys)

docs/source/optimizations.rst

@@ -38,7 +38,9 @@ Running through QCEngine
 
 A basic driver has been implemented in QCEngine. QCEngine is built upon QCElemental which provides input
 validation and standardized input/output. To see the requirements for an Optimziation Input check MolSSI's
-`qcelemental documentation <http://docs.qcarchive.molssi.org/projects/QCElemental/en/stable/api/qcelemental.models.OptimizationInput.html#qcelemental.models.OptimizationInput>`_. NOTE QCElemental assumes atomic units by default::
+`qcelemental documentation <http://docs.qcarchive.molssi.org/projects/QCElemental/en/stable/api/qcelemental.models.OptimizationInput.html#qcelemental.models.OptimizationInput>`_. NOTE QCElemental assumes atomic units by default:
+
+.. code-block:: python
 
     import qcengine as qcng
 
@@ -73,7 +75,9 @@ validation and standardized input/output. To see the requirements for an Optimzi
     result = qcng.compute_procedure(opt_input, "optking")
 
 An explicit example of creating and running an OptimizationInput. Note: Molecule.from_data seems to be the only
-place Angstroms are expected::
+place Angstroms are expected:
+
+.. code-block:: python
 
     import qcengine as qcng
 
@@ -148,7 +152,9 @@ as attributes of the OptHelper instance.
 `CustomHelper` accepts `QCElemental` and `Psi4` molecules while requiring user provided gradients, energies, and possibly hessians. This may
 be useful for implementing a custom optimization driver or procedure using optking.
 
-EngineHelper::
+EngineHelper:
+
+.. code-block:: python
 
     import qcengine as qcng
 
@@ -197,7 +203,9 @@ EngineHelper::
     E = json_output["energies"][-1]
 
 `CustomHelper` can take `psi4` or `qcelemental` molecules. A simple example of a custom optimization loop is
-shown where the gradients are provided from a simple lennard jones potential::
+shown where the gradients are provided from a simple lennard jones potential:
+
+.. code-block:: python
 
     h2o = psi4.geometry(
     """ 

optking/addIntcos.py

@@ -376,14 +376,14 @@ def add_oofp_from_connectivity(C, intcos, geom):
     # Find adjacent atoms
     vertex_atoms = []
     for T in terminal_atoms:
-        vertex_atoms.append(np.where(C[T] == True)[0][0])
+        vertex_atoms.append(np.where(C[T])[0][0])
 
     for (T, V) in zip(terminal_atoms, vertex_atoms):
         if Nneighbors[V] < 3:
             pass
         # Find at least 2 other/side atoms
         side = []
-        for N in np.where(C[V] == True)[0]:
+        for N in np.where(C[V])[0]:
             if N == T:
                 pass
             else:

optking/dimerfrag.py

@@ -132,28 +132,28 @@ def __init__(
         self._A_idx = A_idx
         self._B_idx = B_idx
 
-        if type(A_atoms) != list:
+        if not isinstance(A_atoms, list):
             raise OptError("Atoms argument for frag A should be a list")
         for i, a in enumerate(A_atoms):
-            if type(a) != list:
+            if not isinstance(a, list):
                 raise OptError("Atoms argument for frag A, reference pt. %d should be a list" % (i + 1))
 
-        if type(B_atoms) != list:
+        if not isinstance(B_atoms, list):
             raise OptError("Atoms argument for frag B should be a list")
         for i, b in enumerate(B_atoms):
-            if type(b) != list:
+            if not isinstance(b, list):
                 raise OptError("Atoms argument for frag B, reference pt. %d should be a list" % (i + 1))
 
         if A_weights is None:
             A_weights = []
             for i in range(len(A_atoms)):
                 A_weights.append(len(A_atoms[i]) * [1.0])
         else:
-            if type(A_weights) == list:
+            if isinstance(A_weights, list):
                 if len(A_weights) != len(A_atoms):
                     raise OptError("Number of reference atoms and weights on frag A are inconsistent")
                 for i, w in enumerate(A_weights):
-                    if type(w) != list:
+                    if not isinstance(w, list):
                         raise OptError("Weight for frag A, reference pt. %d should be a list" % (i + 1))
             else:
                 raise OptError("Weights for reference atoms on frag A should be a list")
@@ -163,11 +163,11 @@ def __init__(
             for i in range(len(B_atoms)):
                 B_weights.append(len(B_atoms[i]) * [1.0])
         else:
-            if type(B_weights) == list:
+            if isinstance(B_weights, list):
                 if len(B_weights) != len(B_atoms):
                     raise OptError("Number of reference atoms and weights on frag B are inconsistent")
                 for i, w in enumerate(B_weights):
-                    if type(w) != list:
+                    if not isinstance(w, list):
                         raise OptError("Weight for frag B, reference pt. %d should be a list" % (i + 1))
             else:
                 raise OptError("Weights for reference atoms on frag B should be a list")
@@ -1107,10 +1107,10 @@ def test_orient(NA, NB, printInfo=False, randomSeed=None):
     t = 0.02  # threshold near pi
     q_tar += 0.2
     for i, intco in enumerate(Itest._pseudo_frag.intcos):
-        if type(intco) == bend.Bend:
+        if isinstance(intco, bend.Bend):
             if q_tar[i] > np.pi - t:
                 q_tar[i] -= 0.4
-        elif type(intco) == tors.Tors:
+        elif isinstance(intco, tors.Tors):
             if q_tar[i] > np.pi - t:
                 q_tar[i] -= 0.4
             elif q_tar[i] < -np.pi + t:

optking/hessian.py

@@ -68,14 +68,14 @@ def guess(oMolsys, connectivity=None, guessType="SIMPLE"):
     for DI in oMolsys._dimer_intcos:
         vals = DI.q()
         for i, intco in enumerate(DI._pseudo_frag._intcos):
-            if type(intco) == Stre:
+            if isinstance(intco, Stre):
                 h = 0.007
                 if intco.inverse:
                     h *= pow(1.0 / vals[i], 4)
                     # i should be 0=stretch
-            elif type(intco) == Bend:
+            elif isinstance(intco, Bend):
                 h = 0.003
-            elif type(intco) == Tors:
+            elif isinstance(intco, Tors):
                 h = 0.001
             else:
                 h = 0.111

optking/tests/test_atom.py

@@ -15,7 +15,7 @@ def test_atom():
     success = qc_output['success']
     Etraj = qc_output["trajectory"][-1]['extras']['qcvars']['CURRENT ENERGY']
 
-    assert qc_output['success'] == False
+    assert not qc_output['success']
     assert qc_output["error"]["error_message"] == "There is only 1 atom. Nothing to optimize. Computing energy."
     assert psi4.compare_values(E, RefEnergy, 6, "Atom energy (energies)")
     assert psi4.compare_values(Etraj, RefEnergy, 6, "Atom energy (trajectory)")

optking/tests/test_atom_stepwise.py

@@ -42,7 +42,7 @@ def test_atom_stepwise():
         E = qc_output['energies'][-1]
         Etraj = qc_output["trajectory"][-1]['extras']['qcvars']['CURRENT ENERGY']
 
-        assert qc_output['success'] == False
+        assert not qc_output['success']
         assert qc_output["error"]["error_message"] == "There is only 1 atom. Nothing to optimize. Computing energy."
         assert psi4.compare_values(RefEnergy, E, 6, "Atom energy (energies)")
         assert psi4.compare_values(RefEnergy, E, 6, "Atom energy (trajectory)")

optking/tests/test_ts_opt.py

@@ -31,7 +31,7 @@ def test_hooh_TS(check_iter, option, expected_steps):
     }
     psi4.set_options(psi4options)
 
-    json_output = optking.optimize_psi4("hf")
+    json_output = optking.optimize_psi4("hf", {"step_type": option})
 
     E = json_output["energies"][-1]  # TEST
     # print( '{:15.10f}'.format(E) )
@@ -67,7 +67,7 @@ def test_hooh_TS_zero(check_iter, option, expected_steps):
     }
     psi4.set_options(psi4options)
 
-    json_output = optking.optimize_psi4("hf")
+    json_output = optking.optimize_psi4("hf", {"step_type": option})
 
     E = json_output["energies"][-1]  # TEST
     C2V_TS_ENERGY = -150.774009217562  # TEST