GitLab

to support triclinic cells.

Also extend unitcell.computePeriodicBoxVectors to take Quantity inputs rather
than *just* scalars assumed to be nanometers.

now that triclinics are supported this is important).

Update the Python application layer to use both force constants when building
the AmoebaStretchBendForce.

between 1 and 2 Angstroms, or they fall outside the bounds of the neck lookup
tables.

At least on my Mac, this results in a C++ exception that does not get translated
into a Python exception (i.e., it is not catchable and crashes the application)

topology file with the Amber file parsers. These do *not* currently work for
creating a system.

are NOT numpy arrays.

run_tests to TestingError so users only ever have one exception to catch.

SyntaxWarning otherwise). So move those imports back to the module level and
save any error messages for when the tests are actually run (if ever).

python -m simtk.testInstallation

runs the tests, whereas

python -c "import simtk.testInstallation"

does not.

Also copy the test PDB file to the data/ directory, since we rather need it...

most important and we don't want false positives.

At some point, at least, TP4 was the residue label for water (I think tleap
currently translates most of the water residue labels to WAT, but there are at
least some -- like $AMBERHOME/test/tip4p/prmtop -- that use older labels).

Make sure files are appropriately closed by PDBFile()

Add a test for the various numpy attributes (and the arguments they take) for
the unit module.

Traceback (most recent call last):
  File ".../python/tests/TestNumpyCompatibility.py", line 87, in testNumpyAttributes
    d = self.data.reshape((100, 3))
  File ".../simtk/unit/quantity.py", line 575, in reshape
    return Quantity(self._value.reshape(shape, order=order))
  File ".../simtk/unit/quantity.py", line 142, in __init__
    if value == first_item:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Also add a test for new unit numpy capabilities.

underlying numpy array. This function would be used in instances where, for
instance, a certain API would send a flattened array of coordinates or
velocities with units attached, and you wanted to reshape the array into
(natom, 3) to be consistent with OpenMM's Context object.

arguments and pass them to the numpy function if applicable. That way, users get
the full flexibility of the numpy API on those particular methods AND get the
added benefit that the result has the correct units (and it all runs fast).