GaussianLogReader

Implements a stream based reader for a Gaussian .log file. This is inherits from the FileStreamReader base, and takes a two pronged approach to getting data. First, a block is found in a log file based on a pair of tags. Next, a function (usually based on a StringParser) is applied to this data to convert it into a usable data format. The goal is to move toward wrapping all returned data in a QuantityArray so as to include data type information, too.

You can see the full list of available keys in the GaussianLogComponents module, but currently they are:

  • "Header": the header for the Gaussian job
  • "InputZMatrix": the string of the input Z-matrix
  • "CartesianCoordinates": all the Cartesian coordinates in the file
  • "ZMatCartesianCoordinates": all of the Cartesian coordinate in Z-matrix orientation
  • "StandardCartesianCoordinates": all of the Cartesian coordinates in ‘standard’ orientation
  • "InputCartesianCoordinates": all of the Cartesian coordinates in ‘input’ orientation
  • "ZMatrices": all of the Z-matrices
  • "OptimizationParameters": all of the optimization parameters
  • "MullikenCharges": all of the Mulliken charges
  • "MultipoleMoments": all of the multipole moments
  • "DipoleMoments": all of the dipole moments
  • "OptimizedDipoleMoments": all of the dipole moments from an optimized scan
  • "ScanEnergies": the potential surface information from a scan
  • "OptimizedScanEnergies": the PES from an optimized scan
  • "XMatrix": the anharmonic X-matrix from Gaussian’s style of perturbation theory
  • "Footer": the footer from a calculation

You can add your own types, too. If you need something we don’t have, give GaussianLogComponents a look to see how to add it in.

Properties and Methods

registered_components: OrderedDict
default_keys: tuple
default_ordering: dict
job_default_keys: dict

 

parse(self, keys=None, num=None, reset=False):

The main function we’ll actually use. Parses bits out of a .log file.

  • keys: str or list(str)

    the keys we’d like to read from the log file

  • num: int or None

    for keys with multiple entries, the number of entries to pull

  • :returns: dict

    the data pulled from the log file, strung together as a dict and keyed by the keys

 

get_default_keys(self):

Tries to get the default keys one might be expected to want depending on the type of job as determined from the Header Currently only supports ‘opt’, ‘scan’, and ‘popt’ as job types.

  • :returns: tuple(str)

    key listing

Examples

We’re working on improving this documentation, but in the meantime, here are the unit tests we use

from McUtils.GaussianInterface import GaussianLogReader

def test_GetLogInfo(self):
    with GaussianLogReader(TestManager.test_data("tbhp_030.log")) as reader:
        parse = reader.parse("Header")
    self.assertIn("P", parse["Header"].job)

def test_DefaultLogParse(self):
    with GaussianLogReader(TestManager.test_data("tbhp_030.log")) as reader:
        parse = reader.parse()
    self.assertLess(parse["OptimizedScanEnergies"][1][0], -308)

def test_GetDipoles(self):
    with GaussianLogReader(self.test_log_water) as reader:
        parse = reader.parse("DipoleMoments")
    dips = parse["DipoleMoments"]
    self.assertIsInstance(dips, np.ndarray)
    self.assertEquals(dips.shape, (251, 3))

def test_GaussianLoad(self):
    with GaussianLogReader(self.test_log_water) as reader:
        parse = reader.parse("InputZMatrix")
    zmat = parse["InputZMatrix"]
    self.assertIsInstance(zmat, str)

def test_GaussianAllCartesians(self):
    with GaussianLogReader(self.test_log_water) as reader:
        parse = reader.parse("CartesianCoordinates")
    carts = parse["CartesianCoordinates"]
    self.assertIsInstance(carts[1], np.ndarray)
    self.assertEquals(carts[1].shape, (502, 3, 3))

def test_GaussianCartesians(self):
    with GaussianLogReader(self.test_log_water) as reader:
        parse = reader.parse("CartesianCoordinates", num=15)
    carts = parse["CartesianCoordinates"]
    self.assertIsInstance(carts[1], np.ndarray)
    self.assertEquals(carts[1].shape, (15, 3, 3))

def test_GaussianStandardCartesians(self):
    with GaussianLogReader(self.test_log_water) as reader:
        parse = reader.parse("StandardCartesianCoordinates", num=15)
    carts = parse["StandardCartesianCoordinates"]
    self.assertIsInstance(carts[1], np.ndarray)
    self.assertEquals(carts[1].shape, (15, 3, 3))

def test_GaussianZMatrixCartesians(self):
    with GaussianLogReader(self.test_log_water) as reader:
        parse = reader.parse("ZMatCartesianCoordinates", num=15)
    carts = parse["ZMatCartesianCoordinates"]
    self.assertIsInstance(carts[1], np.ndarray)
    self.assertEquals(carts[1].shape, (15, 3, 3))

def test_GZMatCoords(self):
    with GaussianLogReader(self.test_log_water) as reader:
        parse = reader.parse("ZMatrices", num = 3)
    zmats = parse["ZMatrices"]
    # print(zmats, file=sys.stderr)
    self.assertIsInstance(zmats[0][1][0], str)
    self.assertIsInstance(zmats[1], np.ndarray)
    self.assertEquals(zmats[1].shape, (3, 3))
    self.assertEquals(zmats[2].shape, (3, 3, 3))

def test_ScanEnergies(self):
    with GaussianLogReader(self.test_log_tet) as reader:
        parse = reader.parse("ScanEnergies", num=3)
    engs = parse["ScanEnergies"]
    self.assertIsInstance(engs, dict)
    self.assertIsInstance(engs["values"], np.ndarray)
    self.assertIsInstance(engs["coords"], np.ndarray)
    self.assertEquals(engs["coords"].shape, (5,))
    self.assertEquals(engs["values"].shape, (30, 5))

def test_GZMatCoordsBiggie(self):
    num_pulled = 5
    num_entries = 8
    with GaussianLogReader(self.test_log_h2) as reader:
        parse = reader.parse("ZMatrices", num = num_pulled)
    zmats = parse["ZMatrices"]
    # print(zmats, file=sys.stderr)
    self.assertIsInstance(zmats[1], np.ndarray)
    self.assertEquals(zmats[1].shape, (num_entries, 3))
    self.assertEquals(zmats[2].shape, (num_pulled, num_entries, 3))

def test_OptScanEnergies(self):
    with GaussianLogReader(self.test_log_opt) as reader:
        parse = reader.parse("OptimizedScanEnergies")
    e, c = parse["OptimizedScanEnergies"]
    self.assertIsInstance(e, np.ndarray)
    self.assertEquals(e.shape, (10,))
    self.assertEquals(len(c.keys()), 14)
    self.assertEquals(list(c.values())[0].shape, (10,))

def test_OptDips(self):
    with GaussianLogReader(self.test_log_tet_rel) as reader:
        parse = reader.parse(["OptimizedScanEnergies", "OptimizedDipoleMoments"])
    c = np.array(parse["OptimizedDipoleMoments"])
    self.assertIsInstance(c, np.ndarray)
    self.assertEquals(c.shape, (30,3))

def test_XMatrix(self):
    file=TestManager.test_data('qooh1.log')
    with GaussianLogReader(file) as reader:
        parse = reader.parse("XMatrix")
    X = np.array(parse["XMatrix"])

    self.assertIsInstance(X, np.ndarray)
    self.assertEquals(X.shape, (39, 39))
    self.assertEquals(X[0, 10], -0.126703)
    self.assertEquals(X[33, 26], -0.642702E-1)

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