def read_ensemble(cls, filename, conformational=False):
"""
Alias for read_trajectory.
"""
files = cls.read_trajectory(filename)
ensemble = None
if conformational:
ensemble = cctk.ConformationalEnsemble()
else:
ensemble = cctk.Ensemble()
for f in files:
ensemble.add_molecule(f.molecule)
return ensemble
def test_ensemble_indexing2(self):
path = "test/static/gaussian_file.out"
file = cctk.GaussianFile.read_file(path)
mols = file.ensemble
self.assertTrue(isinstance(mols, cctk.ConformationalEnsemble))
ensemble = cctk.ConformationalEnsemble()
for i, molecule in enumerate(mols.molecules):
ensemble.add_molecule(molecule)
ensemble[molecule, "test_property"] = i
self.assertTrue(len(ensemble) == 3)
self.assertListEqual(list(ensemble[:, "test_property"]), [0, 1, 2])
ensemble = cctk.Ensemble()
for i, molecule in enumerate(mols.molecules):
ensemble.add_molecule(molecule)
ensemble[molecule, "test_property"] = i
self.assertTrue(len(ensemble) == 3)
self.assertListEqual(list(ensemble[:, "test_property"]), [0, 1, 2])
import cctk, sys, yaml, tqdm
# usage: python generate_si.py config.txt output.txt
# config should be tab-separated list of titles and filenames
ensemble = cctk.Ensemble()
titles = list()
with open(sys.argv[1], "r+") as f:
for line in tqdm.tqdm(f.readlines()):
try:
title, path = line.rsplit(' ', 1)
gf = cctk.GaussianFile.read_file(path.strip())
m, p = gf.get_molecule(properties=True)
p["route_card"] = gf.route_card
p["imaginaries"] = gf.imaginaries()
ensemble.add_molecule(m, p)
titles.append(title.strip())
except Exception as e:
print(f"skipping {line} - error {e}")
si_file = cctk.SIFile(ensemble, titles)
si_file.write_file(sys.argv[2])
from tabulate import tabulate
from tqdm import tqdm
#### This is a script to monitor the output of Orca files.
#### In contrast to ``monitor_orca.py``, this script analyzes many files!
#### If the file has not successfully achieved SCF convergence at least once, that file will not display any information.
#### Usage: ``python analyze_orca.py "path/to/output/*.out"``
#### NOTE: It's crucial to wrap the wildcard-containing path in quotes!
#### NOTE: This file will reject any file that contains the string "slurm."
#### Corin Wagen and Eugene Kwan, 2019
results = cctk.Ensemble()
parser = argparse.ArgumentParser(prog="analyze.py")
parser.add_argument("--g", action="store_true")
parser.add_argument("--h", action="store_true")
parser.add_argument("filename")
args = vars(parser.parse_args(sys.argv[1:]))
print("\n\033[3mreading files:\033[0m")
for filename in tqdm(sorted(glob.glob(args["filename"], recursive=True))):
if re.search("slurm", filename):
continue
output_file = cctk.OrcaFile.read_file(filename)
if output_file is None:
def main():
results = cctk.Ensemble()
parser = argparse.ArgumentParser(prog="analyze.py")
parser.add_argument("--g", action="store_true")
parser.add_argument("--h", action="store_true")
parser.add_argument("--standard_state", action="store_true", default=False)
parser.add_argument("--correct_gibbs", action="store_true", default=False)
parser.add_argument("--sort", action="store_true", default=False)
parser.add_argument("filename")
args = vars(parser.parse_args(sys.argv[1:]))
print("\n\033[3mreading files:\033[0m")
files = glob.glob(args["filename"], recursive=True)
pool = mp.Pool(processes=16)
for output_file in tqdm(pool.imap(read, files), total=len(files)):
molecule = None
if output_file is None or (isinstance(output_file, list)
and len(output_file) == 0):
continue
if output_file.successful_terminations:
results.add_molecule(*list(output_file.ensemble.items())[-1])
molecule = output_file.ensemble.molecules[-1]
elif len(output_file.ensemble) > 1:
results.add_molecule(*list(output_file.ensemble.items())[-2])
molecule = output_file.ensemble.molecules[-2]
elif len(output_file.ensemble) == 1:
results.add_molecule(*list(output_file.ensemble.items())[-1])
molecule = output_file.ensemble.molecules[-1]
else:
continue
results[molecule, "iters"] = len(output_file.ensemble)
results[molecule, "success"] = output_file.successful_terminations
results[molecule, "imaginary"] = output_file.imaginaries()
results[molecule, "ts_atoms"] = molecule.atoms_moving_in_imaginary(
return_string=False)
if len(results) == 0:
print("no jobs to analyze!")
exit()
print("\n\n\033[3manalysis:\033[0m\n")
property_names = None
if args["correct_gibbs"]:
property_names = [
"filename", "iters", "energy", "enthalpy",
"quasiharmonic_gibbs_free_energy", "rms_force", "rms_displacement",
"success", "imaginary", "ts_atoms"
]
else:
property_names = [
"filename", "iters", "energy", "enthalpy", "gibbs_free_energy",
"rms_force", "rms_displacement", "success", "imaginary", "ts_atoms"
]
values = results[:, property_names]
if not isinstance(values[0], list):
values = [values]
df = pd.DataFrame(values, columns=property_names)
df.sort_values("filename", inplace=True)
if args["correct_gibbs"]:
df.rename(
columns={"quasiharmonic_gibbs_free_energy": "gibbs_free_energy"},
inplace=True)
if args["g"]:
print(df.columns)
df = df.dropna(subset=["gibbs_free_energy"])
df["rel_energy"] = (df.gibbs_free_energy -
df.gibbs_free_energy.min()) * 627.509469
elif args["h"]:
df = df.dropna(subset=["enthalpy"])
df["rel_energy"] = (df.enthalpy - df.enthalpy.min()) * 627.509469
else:
try:
df["rel_energy"] = (df.energy - df.energy.min()) * 627.509469
except:
df["rel_energy"] = 0
if args["sort"]:
df.sort_values("rel_energy", inplace=True)
df.fillna("")
df.rename(columns={
"rms_displacement": "rms_disp",
"gibbs_free_energy": "GFE"
},
inplace=True)
if args["standard_state"]:
# convert to 1 M standard state = 6.354 e.u. * 298 K / 4184 J/kcal
df["GFE"] = df["GFE"].apply(lambda x: f"{x - (0.4526 / 627.509):.5f}"
if isinstance(x, float) else "")
else:
df["GFE"] = df["GFE"].apply(lambda x: f"{x:.5f}"
if isinstance(x, float) else "")
df["filename"] = df["filename"].apply(lambda x: x[-60:])
df["rms_force"] = df["rms_force"].apply(
lambda x: f"\033[92m{x}\033[0m"
if float(x or 0) < 0.0001 else f"\033[93m{x}\033[0m")
df["rms_disp"] = df["rms_disp"].apply(
lambda x: f"\033[92m{x}\033[0m"
if float(x or 0) < 0.003 else f"\033[93m{x}\033[0m")
df["success"] = df["success"].apply(lambda x: f"\033[92m{x}\033[0m"
if x else f"\033[93m{x}\033[0m")
df.columns = [f"\033[1m{c}\033[0m" for c in df.columns]
print(tabulate(df, headers="keys", tablefmt="presto", floatfmt=".5f"))